ExpeTrans: LLMs Are Experiential Transfer Learners

Bibo Cai; Bing Qin; Jinglong Gao; Lingxiao Zou; Ting Liu; Xiao Ding

REVIEW 3 major objections 4 minor 1 cited by

ExpeTrans: LLMs Are Experiential Transfer Learners

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read LLMs can act as experiential transfer learners, reusing textual task-solving experience from existing datasets on new task types without per-task human effort.

desk verdict A genuinely new cross-task experience-transfer framework, but the main experiment is contaminated because the same 6,500 examples are both scored and used to build the experience memory. read the letter →

arxiv 2505.23191 v1 pith:JGPX5OTH submitted 2025-05-29 cs.CL cs.AI

Jinglong Gao , Xiao Ding , Lingxiao Zou , Bibo Cai , Bing Qin , Ting Liu This is my paper

classification cs.CLcs.AI

keywords experientialtransferlearninglargelanguagemodelstask-wiseexperiencememorystructure-mappingtheorycross-taskzero-shotchain-of-thoughtsourcetaskselectionprompt-basedreasoning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

ExpeTrans sets out to show that a large language model can learn from experience the way people do when they face a new task: borrow what worked on similar tasks instead of starting from scratch. The framework stores each source example as a small task with a stated goal, a stated procedure, and verified textual insights, then chooses source tasks by how close their goals and procedures are to the new query. On 13 classification datasets the paper reports an average accuracy of 63.8%, compared with 54.9% for Zero-shot-CoT. If the result holds, users would not need to collect task-specific experience for each new query type; existing labeled datasets could serve as a reusable reservoir of task-solving knowledge.

What carries the argument

The machinery is a task-wise experience memory plus a two-dimensional task-similarity selector. Each memory entry is a single source example labeled with task function and task process, and with experience insights that were verified to fix a hard example. Source selection ranks candidates separately by BM25 over function and over process, combines the ranks with a harmonic mean, and lets the LLM pick from the top candidates; then a transfer prompt rewrites the source insights onto the target task's function and process. The design choice of not showing queries or examples during transfer is what keeps the transferred insights general rather than format-bound.

What would settle it

Run ExpeTrans with a strict data split: use one portion of each dataset only for experience accumulation and a disjoint portion only for testing; if average accuracy drops to the Zero-shot-CoT level (about 54.9%) or below, the cross-task transfer claim would be unsupported.

Watch

Extended reading notes

Core claim

The paper's central claim is that LLMs can transfer textual task-solving experience across tasks, and that the transfer is most reliable when tasks are represented by their function (the goal) and their process (the steps). ExpeTrans operationalizes that idea by summarizing, for every example in existing labeled datasets, a task function, a task process, and experience insights that survive a self-verification step; these are stored as candidate source tasks. For a new query, the framework generates the target task's function and process, ranks the candidates by BM25 on both dimensions, merges the rankings by harmonic mean, asks the model to select suitable source tasks, adapts their insights to the target task, and finally answers the query with the most relevant transferred insights. The paper attributes the improvement over zero-shot prompting and cross-task in-context learning to the use of abstract, verifiable experience rather than concrete demonstrations.

Load-bearing premise

The evaluation uses the same examples for accumulating source experiences and for testing, so the measured improvement assumes that overlap does not inflate the transfer results.

Editorial extensions

If this is right

Users of an LLM could answer a newly encountered task type by drawing on already-annotated datasets, with no per-task human labeling or pseudo-dataset generation.
Because experience summaries are stored once and reused, the cost of acquiring source experience is amortized over many queries rather than repeated for each new task.
Cross-task transfer through abstract experience should be less sensitive to differences in example format than in-context learning, which the paper's comparison with Cross-ICL* supports.
The skip-transfer mechanism prevents the framework from forcing irrelevant experience onto a query, so it can fall back to zero-shot chain-of-thought when no suitable source task is found or the query is simple.
The framework's effectiveness degrades gracefully as source tasks become less similar, but remains above zero-shot prompting even when transfer is limited to other categories.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Outside the paper: the reported 63.8% average may be optimistic because the same examples used to accumulate experience are also the test examples; a held-out split could shrink the gap to Zero-shot-CoT.
Outside the paper: the framework's benefit appears largest on models and tasks that are not already proficient, so the natural next experiment is measuring transfer gain against baseline proficiency on a wider model range.
Outside the paper: replacing BM25 with a stronger learned retriever improved accuracy in the paper's comparison, suggesting the source-selection step may constrain the framework more than experience generation does.
Outside the paper: the framework is demonstrated on classification; extending it to generative and extractive tasks is sketched with different verification metrics, but whether experience verified by ROUGE-L or F1 transfers as cleanly remains an open test.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

ExpeTrans is a plausible and in parts elegant framework for cross-task transfer of textual experiences, but the main experimental claim rests on a contaminated evaluation. Section 3.1 states plainly that all 6,500 test examples are also used for experience accumulation; the only guard is that a query cannot select a source task from its own dataset. That means nearly every scored example has already contributed, via other datasets, to the experiences that guide its own answer. The reported 63.8% vs 54.9% accuracy gain does not measure transfer to unseen queries.

Credit where due: the cross-task experience-transfer setting is new relative to Expel, Grimoire, and SEGPT. The SMT-inspired selection by task function and process, the per-example fine-grained memory, and the skip-transfer mechanism are well motivated. The ablation study is unusually thorough: Figures 3–6 and Tables 4–6 cover granularity, verification count, number of insights, candidate count, and the individual contributions of function and process similarity. The authors also honestly report a 1.3% failure rate from noisy experiences and extend the framework to generative and extractive tasks.

The soft spot is not minor. Because experiences are verified with Prompt 4 on the exact test examples from which they were summarized, the method effectively has access to test-derived information. The cross-validation description in Section 3.1 does not change this: the pool is still the test pool. Hyperparameters K and M are also chosen by inspecting Figures 5–6 on the same pooled examples, and no significance tests are reported. A clean held-out evaluation—for instance, accumulating experiences from one half of each dataset and testing on the other half, or using external source datasets—is necessary before the transfer claim is believable.

Bottom line: I would not cite this result as evidence that experience transfer works. But the idea and the analysis are serious enough that a proper referee should engage with it. My recommendation: reject in current form, and invite the authors to rerun with a clean split. If the gains survive that rerun, this becomes a decent paper.

Referee Report

3 major / 4 minor

Summary. The paper proposes ExpeTrans, an autonomous framework that accumulates textual task-solving experiences from existing labeled datasets into a task-wise memory and then, for each new query, selects similar source tasks using task function and process similarity, transfers their stored experiences to the target task, and prompts an LLM to answer with the transferred experience. The framework is evaluated on 13 classification datasets, reporting an average accuracy of 63.8% versus 54.9% for Zero-shot-CoT. The authors also provide ablation studies, cost analysis, and case studies.

Significance. If the evaluation were valid, the paper would offer a practical way to reuse textual experiences across NLP tasks without per-task human effort, and the detailed analysis of source task selection and experience verification would be useful to the community. The paper ships a fairly complete method section with prompts and memory examples, and it includes extensive ablations. However, the central empirical claim is undermined by the reuse of the test examples as the source of accumulated experience, so the reported gains do not currently establish cross-task transfer to unseen queries.

major comments (3)

[§3.1] The experimental protocol uses the same 500 examples per dataset both as the test set and as the source for experience accumulation: the paper states, 'In addition to testing, all of these examples are also used for our experience accumulation.' Since the experience memory is built from the full test pool (with only same-dataset source exclusion), the accuracy reported in Table 1 is measured on queries whose labels have already been used to generate, verify, and select experiences for other queries in the same pool. This is test-set contamination and does not support the claim that ExpeTrans generalizes to unseen target queries. A clean held-out evaluation, for example building the memory from training or development splits and testing on disjoint examples, is required before the central claim can be accepted.
[§2.2.2 and §3.2] The experience verification step (Prompt 4) checks whether a candidate experience enables the model to correctly answer the exact hard example from the test pool, and hyperparameters K and M are selected by monitoring performance on the same pooled examples (Figures 5 and 6). This means the framework is tuned with respect to the test data, further inflating the reported improvements and making the comparison against Zero-shot-CoT (which does not use test labels) unfair.
[Table 1 and §3.3] The comparison with baselines is also affected by the contaminated protocol: SEGPT-T is described as summarizing experience from 'our test data instead of its noisy pseudo-dataset,' and Cross-ICL selects demonstrations from the other test datasets. Consequently, all methods operate with access to test-derived source material, and the relative ordering in Table 1 does not measure generalization ability. The 'Low' and 'Minimal' settings in Table 2 still reuse test data from other categories, so they do not correct this issue.

minor comments (4)

[§2.3.1] There is a typo: 'task function and processe of the target task' should read 'task function and process of the target task.'
[Table 1] The cell '57.873.653.2' for the Cross-ICL row on the Textual Reasoning columns appears to be a formatting error; likely several numbers are conjoined and should be separated.
[§3.5] The term 'Minimal' for the third transferability level is not defined in the main text until the caption of Table 2; consider defining it explicitly in §3.5 for clarity.
[Appendix E] The discussion of the slow-think LLM experiments reports only a brief anecdotal result and does not provide a table or detailed configuration; adding specifics would improve reproducibility.

Circularity Check

1 steps flagged · score 6.0 of 10

Experience memory is built from and verified on the same 6,500 examples that are later scored, so the Table 1 improvement is not a clean prediction for unseen target tasks.

fitted input called prediction [Section 2.2.2 (Experience Accumulation Process), Section 3.1 (Datasets and Evaluation Metrics), and Table 1]
"We randomly select 500 examples from each dataset and mix them as the test data, which contains 6,500 examples. In addition to testing, all of these examples are also used for our experience accumulation. ... 4) for the experiences, we use Prompt 4 to verify their effectiveness, retaining only those that enable ChatGPT to correctly respond to the current hard example."

The 'current hard example' in Step 4 is one of the 6,500 test examples, because Section 3.1 states that all test examples are also used for experience accumulation. The memory is therefore constructed by reflecting on the exact items that Table 1 later scores, and each stored experience is selected by checking that it produces the correct label on one of those items. Excluding same-dataset source tasks merely prevents a query from retrieving a memory entry built from its own dataset; it does not prevent the other 12 datasets' test labels from shaping the experiences that are transferred. Thus the reported 63.8% average is measured on data that directly determined the content of the experience memory, so it does not establish generalization to newly encountered target queries.

full rationale

The central derivation chain of ExpeTrans is otherwise self-contained: task functions, processes, and experiences are produced by LLM prompts, source selection uses BM25 and Prompt 5, and the method is compared with baselines. I found no load-bearing self-citation or imported uniqueness theorem. However, the evaluation protocol contaminates the test set: Section 3.1 explicitly uses all 6,500 test examples for experience accumulation, and Section 2.2.2 verifies each summarized experience by checking whether it correctly answers the same hard example. This is a fitted-input-called-prediction pattern: the experience memory is fitted to the scored examples, so Table 1's 63.8% vs 54.9% cannot be read as evidence for transfer to unseen target tasks. The paper's Limitations section notes scope and cost restrictions but does not acknowledge this data overlap. A held-out evaluation with separate source and target examples is required to support the paper's central claim. Because the framework itself has independent components but the headline result is compromised by test-set-derived memory, a partial-circularity score of 6 is appropriate.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central method relies on SMT-inspired similarity, LLM self-reflection, and a memory of task functions, processes, and experiences. Hyperparameters K, M, and verification count are tuned on the test data, and the test set itself is used to build the memory, which undermines the independence of the evaluation.

free parameters (4)

K (number of candidate source tasks) = 10
Manually selected; Figure 6 on test data shows performance slightly improves with K up to 50, but 10 is chosen as a tradeoff with inference time. Tuned on the test set.
M (number of insights used in final prompt) = 5
Manually selected; Figure 5 on test data shows performance improves then stabilizes with more insights, and 5 is chosen as the default. Tuned on the test set.
Verification times (Prompt 4) = 3
Figure 4 shows performance peaks at 3 verification passes; chosen based on test data.
temperature = 1 (default)
Default API value; experiments average 3 rounds. Not explicitly tuned.

assumptions (4)

domain assumption Structure-Mapping Theory (SMT) applies to LLM experience transfer: task function and process similarity are the key dimensions for transferability.
Used in Section 2.3.1 to design source task selection; assumed without evidence that these two dimensions suffice for LLMs.
domain assumption Experiences summarized from hard examples via LLM self-reflection are reliable and transferable across tasks.
Core to Sections 2.2.2 and 2.3.3; relies on LLM ability to reflect and adapt experience.
domain assumption BM25 lexical similarity on LLM-generated task function and process text is a valid proxy for transferability.
Used in Section 2.3.1 to rank candidate source tasks.
domain assumption The 13 datasets and 500-example sampling are representative of NLP tasks and sufficient to support the general claim.
The empirical claim is based on this benchmark selection.

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Cite this review

Pith. "Pith review of ExpeTrans: LLMs Are Experiential Transfer Learners." pith.science (2026). https://pith.science/paper/JGPX5OTH

@misc{pith2026250523191,
  author       = {Pith},
  title        = {Pith review of: ExpeTrans: LLMs Are Experiential Transfer Learners},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JGPX5OTH}},
  note         = {Machine review of arXiv:2505.23191}
}

read the original abstract

Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance. However, previous methods rely on substantial human labor or time to gather such experiences for each task, which is impractical given the growing variety of task types in user queries to LLMs. To address this issue, we design an autonomous experience transfer framework to explore whether LLMs can mimic human cognitive intelligence to autonomously transfer experience from existing source tasks to newly encountered target tasks. This not only allows the acquisition of experience without extensive costs of previous methods, but also offers a novel path for the generalization of LLMs. Experimental results on 13 datasets demonstrate that our framework effectively improves the performance of LLMs. Furthermore, we provide a detailed analysis of each module in the framework.

Figures

Figures reproduced from arXiv: 2505.23191 by the authors.

**Figure 1.** An example of LLMs inference guided by textual experience. query, Gao et al. (2024a) utilizes LLMs to generate lots of similar queries and assigns labels to them to obtain a pseudo-dataset (taking 3 to 8 minutes per user query), from which the experience is then summarized. However, whenever these methods encounter a new task, they require substantial human labor or time to gain experiences for that task. Given the… view at source ↗

**Figure 2.** The framework of our proposed ExpeTrans. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** The impact of verification times for summa [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

Figures from the paper (5 more)

**Figure 3.** Figure 3: The impact of the number of examples in each dataset used for experience accumulation of our framework based on GPT-3.5. dozen examples; 3) -w/ Overall, all examples in a dataset are treated as a single source task, which aligns with most previous methods. We find that…

**Figure 5.** Figure 5: The impact of the number of insights used [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: The effect of the number of candidates in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Performance of the ICL and out framework [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

**Figure 8.** Figure 8: Performance (%) of our framework based on [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

CrossICL: Cross-Task In-Context Learning via Unsupervised Demonstration Transfer
cs.CL 2025-05 conditional novelty 6.0 of 10

CrossICL reuses demonstrations from similar source tasks, rewriting them into the target task's format, and improves zero-shot ICL on Super-NI without target-task annotations.

Reference graph

Works this paper leans on

42 extracted references · 39 canonical work pages · cited by 1 Pith paper

[1]

− This indicates that the award can be won by either a duo or a group, implying that both categories are included under the same award

**Premise**: The song won the Grammy Award for Best Rap Performance by a Duo or Group in 2010. − This indicates that the award can be won by either a duo or a group, implying that both categories are included under the same award

work page 2010
[2]

Best Rap Performance by a Group

**Hypothesis**: The song won the Grammy Award for Best Rap Performance by a Group in 2010. − This states that the award the song won was specifically for "Best Rap Performance by a Group."

work page 2010
[3]

We also perform a brief test with a slow think LLM

deepseek-llm-7b-chat (DeepSeek-AI et al., 2024); 4) llama-3.1-8B-instruct (Dubey et al., 2024); 5) DeepSeek-V3 (Liu et al., 2024); 6)GPT-4 (Hurst et al., 2024). We also perform a brief test with a slow think LLM. Results are shown in Ta- ble 9. It can be observed that: Firstly, the model does not necessarily perform better with our method just because it ...

work page 2024
[4]

Accuracy

Transferred experience may perform better on more novel and unexpected tasks for LLMs. Con- sidering the increased costs associated with larger LLMs, the value of our method may diminish when applied to such larger LLMs. Our future work will focus on further enhancing the performance of our framework on smaller LLMs while reducing its computational cost o...

work page 2015
[5]

Duo or Group

**Contrasting elements**: − The use of the term "Duo or Group" in the premise contrasts with the hypothesis' focus solely on "Group." Therefore, we cannot ascertain from the premise alone that it was strictly a group that won. Given this analysis, we conclude that the hypothesis cannot be definitively inferred from the premise, since the premise allows fo...

work page 2010
[8]

Duo or Group,

**Comparison of details**: − The premise explicitly mentions "Duo or Group," which means the song could win in either category but does not confirm it was specifically awarded to a group only

work page
[9]

**Logical inference**: − The hypothesis is a specific claim about the type of group that won the award (it claims the award was won by a group), while the premise indicates that the song could have been recognized in both categories (duo or group)

work page
[11]

− Event 2: The exhibition was shown at The Copley Society of Art in Boston

**Identify the Events**: − Event 1: The exhibition took place at the Art Institute of Chicago. − Event 2: The exhibition was shown at The Copley Society of Art in Boston. − Event 3: Due to a lack of space, all the work by American artists was removed

work page

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[12]

the exhibition,

**Determine the Subject and Object**: − For Event 1 and Event 2, the subject is "the exhibition," and the object is the location where it was shown. − In Event 3, the subject is "the lack of space," and the object is "all the work by American artists."
[13]

lack of space

**Evaluating Causal Relationships**: − The exhibition being shown at different locations is not directly causing the removal of the artwork. Rather, it is the "lack of space" that is cited as the reason for the removal. − Therefore, the exhibition itself does not have a causal...
[14]

exhibition

**Conclusion**: Based on this analysis, we can conclude that there is no direct causal relationship between the event of the " exhibition" and the event of "removed." The removal happened due to an external factor (lack of space) that is not directly related to the exhibition ...
[15]

**Perspective of the 82−Year−Old**: − At 82, this individual has witnessed significant political changes over a long period. His reflections on the changes in politics might suggest a sense of nostalgia or critique about how things have evolved, which could indicate resistance...
[16]

This age group may be more open and flexible towards change, as they were raised in a time where change has been a constant aspect of society (e.g., technology, social norms)

**Perspective of the 29−Year−Old**: − Being 29 years old, this person likely has experienced different dynamics and influences in politics, possibly adapting to rapid changes that younger generations often face. This age group may be more open and flexible towards change, as t...
[17]

The younger person's response to the older man's comments isn't provided, making it challenging to directly assess their attitudes

**Cues from the Conversation**: − Since the context only specifies that the older man was speaking about change, it does not clarify whether he is embracing it or lamenting it. The younger person's response to the older man's comments isn't provided, making it challenging to d...
[18]

Who embraces change?

**Personal Experiences and Age**: − Age does play a significant role in one's perspective towards change. The older man may reflect on past experiences and feel hesitant or critical towards contemporary changes, while the younger person might feel more inclined to embrace chan...

1920
[19]

Read and understand the tweet text provided
[20]

Analyze the language used in the tweet to determine the sentiment expressed (in this case, towards the death penalty or capital punishment)
[21]

Consider the tone and emotions conveyed in the text to decide whether the sentiment is negative, neutral, or positive
[22]

Choose the correct answer (Option A: negative, Option B: neutral, Option C: positive) based on the sentiment expressed in the tweet text. In this specific task example, one would need to analyze the sentiment towards the death penalty or capital punishment in the given tweet t...
[23]

Read the Text: Understand the tweet or sentence fully
[24]

Identify Sentiment Indicators: Look for words and phrases indicating positive, negative, or neutral sentiments
[25]

Contextual Analysis: Consider the overall context and intent of the message
[26]

Match to Options: Compare the sentiment to the provided options (negative, neutral, positive)
[27]

death penalty

Select the Correct Option: Choose the option that best fits the identified sentiment. Prompt 3: guide ChatGPT in reflecting on its incorrect answer for the hard example, summarizing experiences about how to solve the task and avoid errors. USER: [the Zero-shot CoT prompt of th...
[28]

The tweet text presents the topic of death penalty or capital punishment in a matter−of−fact manner without explicitly expressing a positive or negative stance
[29]

The language used in the tweet text is informative and descriptive, focusing on defining what the death penalty or capital punishment is without clearly indicating a personal opinion or emotional reaction
[30]

It is more informative and objective in tone

There are no explicit emotional cues or subjective language in the tweet text that would lean towards a specific sentiment like positive or negative. It is more informative and objective in tone
[31]

The tweet text mainly serves to provide a definition or explanation of corporal punishment, specifically the aspect of taking someone's life as a form of punishment, without introducing strong emotional or judgmental language
[32]

Therefore, based on these points, the correct sentiment for the given tweet text is **neutral (Option B)**

Given the lack of personal opinion, emotional language, or evident bias towards a particular view, the overall sentiment in the tweet text can be considered neutral. Therefore, based on these points, the correct sentiment for the given tweet text is **neutral (Option B)**. Tha...
[33]

Look for emotional cues: Pay attention to emotional language, personal opinions, or subjective expressions within the text to determine the sentiment more accurately
[34]

Evaluate objectivity: Consider the overall objectivity of the text and whether it presents information in a neutral, positive, or negative light
[35]

judgmental language: Distinguish between descriptive language that informs about a topic and judgmental language that expresses a clear opinion or sentiment

Focus on descriptive vs. judgmental language: Distinguish between descriptive language that informs about a topic and judgmental language that expresses a clear opinion or sentiment
[36]

Analyze context: Consider the context in which the topic is discussed to gauge the overall sentiment accurately
[37]

By implementing these insights, strategies, or knowledge in sentiment analysis tasks, you can enhance the accuracy of determining the sentiment expressed in written content

Prioritize clear indicators: Rely on clear indicators such as specific emotional cues or strong language that reveal the sentiment expressed in the text. By implementing these insights, strategies, or knowledge in sentiment analysis tasks, you can enhance the accuracy of deter...
[38]

Pay attention to emotional cues in the text
[39]

Evaluate the objectivity of the content
[40]

Differentiate between descriptive and judgmental language
[41]

Consider the context of the discussion
[42]

selected task ids

Prioritize clear indicators for sentiment analysis. Prompt 4: verify the effectiveness of the summarized experiences. USER: [the hard example] Note: I provide you with the following insights that can help you avoid mistakes, and you must carefully refer to these insights: [one...
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Sara Rosenthal, Noura Farra, and Preslav Nakov

Okapi at trec-3.Nist Special Publication Sp, 109:109. Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017. SemEval-2017 task 4: Sentiment analysis in Twitter. InProceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 502– 518, Vancouver, Ca...

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Anwoy Chatterjee, Eshaan Tanwar, Subhabrata Dutta, and Tanmoy Chakraborty

A survey on evaluation of large language mod- els.Preprint, arXiv:2307.03109. Anwoy Chatterjee, Eshaan Tanwar, Subhabrata Dutta, and Tanmoy Chakraborty. 2024. Language models can exploit cross-task in-context learning for data- scarce novel tasks. InProceedings of the 62nd An-...

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arXiv preprint arXiv:2409.15700

Making text embedders few-shot learners. arXiv preprint arXiv:2409.15700. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. 2024. Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437. Wei...

2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

− This indicates that the award can be won by either a duo or a group, implying that both categories are included under the same award

**Premise**: The song won the Grammy Award for Best Rap Performance by a Duo or Group in 2010. − This indicates that the award can be won by either a duo or a group, implying that both categories are included under the same award

work page 2010

[2] [2]

Best Rap Performance by a Group

**Hypothesis**: The song won the Grammy Award for Best Rap Performance by a Group in 2010. − This states that the award the song won was specifically for "Best Rap Performance by a Group."

work page 2010

[3] [3]

We also perform a brief test with a slow think LLM

deepseek-llm-7b-chat (DeepSeek-AI et al., 2024); 4) llama-3.1-8B-instruct (Dubey et al., 2024); 5) DeepSeek-V3 (Liu et al., 2024); 6)GPT-4 (Hurst et al., 2024). We also perform a brief test with a slow think LLM. Results are shown in Ta- ble 9. It can be observed that: Firstly, the model does not necessarily perform better with our method just because it ...

work page 2024

[4] [4]

Accuracy

Transferred experience may perform better on more novel and unexpected tasks for LLMs. Con- sidering the increased costs associated with larger LLMs, the value of our method may diminish when applied to such larger LLMs. Our future work will focus on further enhancing the performance of our framework on smaller LLMs while reducing its computational cost o...

work page 2015

[5] [5]

Duo or Group

**Contrasting elements**: − The use of the term "Duo or Group" in the premise contrasts with the hypothesis' focus solely on "Group." Therefore, we cannot ascertain from the premise alone that it was strictly a group that won. Given this analysis, we conclude that the hypothesis cannot be definitively inferred from the premise, since the premise allows fo...

work page 2010

[6] [8]

Duo or Group,

**Comparison of details**: − The premise explicitly mentions "Duo or Group," which means the song could win in either category but does not confirm it was specifically awarded to a group only

work page

[7] [9]

**Logical inference**: − The hypothesis is a specific claim about the type of group that won the award (it claims the award was won by a group), while the premise indicates that the song could have been recognized in both categories (duo or group)

work page

[8] [11]

− Event 2: The exhibition was shown at The Copley Society of Art in Boston

**Identify the Events**: − Event 1: The exhibition took place at the Art Institute of Chicago. − Event 2: The exhibition was shown at The Copley Society of Art in Boston. − Event 3: Due to a lack of space, all the work by American artists was removed

work page