SHALA-LLM: Smartly Handling Ambiguous Labels in Aligning LLMs

Ashley Wang; Jingyao Wu; Keane Ong; Paul Pu Liang; Rosalind Picard

arxiv: 2606.05376 · v1 · pith:HUB2OSCKnew · submitted 2026-06-03 · 💻 cs.LG

SHALA-LLM: Smartly Handling Ambiguous Labels in Aligning LLMs

Jingyao Wu , Ashley Wang , Keane Ong , Paul Pu Liang , Rosalind Picard This is my paper

Pith reviewed 2026-06-28 07:08 UTC · model grok-4.3

classification 💻 cs.LG

keywords LLM alignmentlabel ambiguityannotator disagreementreinforcement learningnatural language inferenceemotion recognitionlabel distribution

0 comments

The pith

A reinforcement learning method lets LLMs learn from full annotator label distributions on ambiguous tasks instead of single labels.

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

Many human-centered tasks produce genuine label ambiguity because different annotators assign different labels to the same input for defensible reasons. Existing LLM alignment methods train toward one chosen label and discard disagreement as noise. SHALA-LLM instead builds a reinforcement learning loop that directly targets the distribution of annotator labels and automatically spends more optimization steps on the most contested examples. This produces models whose outputs more closely track the spread of human judgments while also raising standard classification metrics. A reader would care because real deployments require LLMs to flag or represent contested cases rather than silently pick one side.

Core claim

SHALA-LLM is a reinforcement learning framework that enables LLMs to learn directly from annotator label distributions rather than single labels by dynamically prioritizing highly ambiguous samples during optimization. Experiments on ambiguity-sensitive NLI and ER benchmarks, including ChaosNLI, GoEmotions, and MSP-Podcast, show that the method improves agreement with annotator distributions, for example reducing Jensen-Shannon Distance by up to 62.1 percent on ChaosNLI, while simultaneously raising F1 by up to 16.7 percent.

What carries the argument

The SHALA-LLM reinforcement learning framework that learns from annotator label distributions while dynamically prioritizing the most ambiguous samples during optimization.

If this is right

Model outputs move closer to the full range of human judgments on inputs where annotators disagree.
Standard classification performance rises at the same time rather than trading off against distribution matching.
The gains appear on both natural language inference and emotion recognition tasks.
Dynamic prioritization automatically focuses training effort on samples with the largest annotator variance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Models trained this way may output fewer overconfident answers on genuinely contested questions.
The prioritization mechanism could be tested in other alignment methods that do not use reinforcement learning.
The approach suggests a route to LLMs that preserve rather than collapse human variability on subjective tasks.

Load-bearing premise

Treating annotator disagreement as useful information that can be leveraged inside reinforcement learning will improve distribution matching without introducing new optimization instabilities or biases.

What would settle it

Applying SHALA-LLM to ChaosNLI or a comparable benchmark and observing no reduction in Jensen-Shannon Distance to the annotator distribution or a drop in F1 score would show the claimed benefits do not hold.

Figures

Figures reproduced from arXiv: 2606.05376 by Ashley Wang, Jingyao Wu, Keane Ong, Paul Pu Liang, Rosalind Picard.

**Figure 1.** Figure 1: Overview of proposed SHALA-LLM: SMARTLY HANDLING AMBIGUOUS LABELS IN ALIGNING LLMS. Given ambiguity-sensitive tasks such as NLI and ER (a), annotations from multiple annotators are aggregated into empirical label distributions. SHALA-LLM prompts the LLM to verbalize probability distributions over candidate classes (b-a), which are directly aligned with annotator distributions through SHALA reward (b-b). Ro… view at source ↗

**Figure 2.** Figure 2: Performance comparison across different ambiguity levels on the ChaosNLI dataset. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Category-level performance analysis on the ChaosNLI dataset across different classes. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Performance comparison across different ambiguity levels on the [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗

**Figure 5.** Figure 5: Performance comparison across different ambiguity levels on the [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗

**Figure 6.** Figure 6: Performance comparison across different emotion classes on the [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗

**Figure 7.** Figure 7: Performance comparison across different emotion classes on the [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗

read the original abstract

Many human-centered tasks, including natural language inference (NLI) and emotion recognition (ER), have multiple plausible interpretations, leading to label ambiguity and challenging disagreements across human annotators. As LLMs are increasingly deployed in real-world settings, faithfully modeling such ambiguity is essential to identify contested inputs, preserve variability in ambiguous cases, and capture the full distribution of human judgments. Yet, existing LLM alignment approaches have predominantly assumed a single correct label, excluding annotator disagreement during optimization. Instead of treating this ambiguity as noise, we show how to treat it as information that improves model behavior through a new algorithm called SMARTLY HANDLING AMBIGUOUS LABELS IN ALIGNING LLMS (SHALA-LLM). This reinforcement learning framework provides a new way for LLMs to learn directly from annotator distributions while dynamically prioritizing highly ambiguous samples during optimization. Experiments on ambiguity-sensitive NLI and ER benchmarks, including ChaosNLI, GoEmotions, and MSP-Podcast, demonstrate that SHALA-LLM improves agreement with annotator label distributions, e.g. on ChaosNLI, it reduces Jensen-Shannon Distance by up to 62.1%. At the same time, SHALA-LLM improves F1 by up to 16.7%, showing that modeling annotator disagreement can also strengthen classification performance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SHALA-LLM applies RL alignment to annotator label distributions with dynamic prioritization of ambiguous samples and reports measurable gains on ChaosNLI and emotion benchmarks.

read the letter

The paper's main move is to replace single-label targets in LLM alignment with the full annotator distribution and add a mechanism that up-weights ambiguous examples during RL training. This produces the reported drops in Jensen-Shannon distance and the F1 lifts on ChaosNLI, GoEmotions, and MSP-Podcast.

It correctly identifies that many human-centered tasks have genuine label spread rather than noise, and it shows that modeling that spread can improve both distribution match and downstream classification. The empirical framing is direct and the numbers are large enough to notice.

The soft spots are in the method description and the evaluation. The abstract leaves the reward formulation, the exact prioritization rule, and the base RL algorithm unspecified, so it is hard to judge whether the combination is a genuine advance or a sensible but incremental tweak on existing distribution-learning and curriculum ideas. No ablations or significance tests are mentioned, which makes it difficult to know how much each component drives the gains or whether the results are stable. The work is empirical rather than theoretical, so those gaps matter.

This paper is for groups already working on alignment for subjective tasks or on soft-label methods in NLP. A reader who needs a concrete recipe for handling annotator disagreement in NLI or emotion data would get practical value from the experiments. It deserves a serious referee because the problem is timely, the claims are testable, and the reported improvements are large enough to warrant checking the implementation details and baselines.

Referee Report

1 major / 0 minor

Summary. The paper proposes SHALA-LLM, a reinforcement learning framework for aligning LLMs on tasks with label ambiguity (NLI and emotion recognition). Instead of assuming a single correct label, the method learns directly from annotator label distributions and dynamically prioritizes highly ambiguous samples during optimization. Experiments on ChaosNLI, GoEmotions, and MSP-Podcast are reported to show improved agreement with annotator distributions (e.g., up to 62.1% reduction in Jensen-Shannon Distance on ChaosNLI) while also improving F1 scores (up to 16.7%).

Significance. If the results hold after full methodological details and controls are provided, the work would be significant for LLM alignment in human-centered tasks. Treating annotator disagreement as signal rather than noise, combined with RL-based dynamic prioritization, offers a concrete way to model judgment distributions instead of forcing single-label outputs. This could affect downstream applications that require faithful representation of ambiguity.

major comments (1)

Abstract: the central performance claims (62.1% JSD reduction and 16.7% F1 improvement on ChaosNLI) are presented without any description of the RL objective, reward formulation, baseline methods, statistical significance tests, or ablation studies. These omissions are load-bearing because the abstract supplies the only quantitative evidence for the claim that modeling ambiguity improves both distributional agreement and classification performance.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We respond point-by-point to the single major comment below.

read point-by-point responses

Referee: [—] Abstract: the central performance claims (62.1% JSD reduction and 16.7% F1 improvement on ChaosNLI) are presented without any description of the RL objective, reward formulation, baseline methods, statistical significance tests, or ablation studies. These omissions are load-bearing because the abstract supplies the only quantitative evidence for the claim that modeling ambiguity improves both distributional agreement and classification performance.

Authors: We agree that the abstract is highly concise and does not describe the RL objective, reward, baselines, significance tests, or ablations. The full manuscript provides these in Section 3 (RL objective defined as expected reward equal to negative JSD to the annotator distribution plus a KL penalty; reward formulation uses soft labels from annotator distributions; dynamic prioritization via ambiguity entropy in the replay buffer) and Section 4 (baselines include standard RLHF/PPO, DPO, and supervised fine-tuning on majority labels; results include means and standard deviations over 5 random seeds with paired t-tests for significance; ablations on the prioritization component are in Section 5.2). To address the concern that the abstract is the sole quantitative evidence presented to readers, we will revise the abstract to include one additional sentence briefly noting the RL framework, distributional reward, and comparison to strong baselines. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper introduces an RL-based algorithm (SHALA-LLM) for modeling annotator label distributions and reports empirical improvements on benchmarks (ChaosNLI JSD reduction up to 62.1%, F1 gain up to 16.7%). No equations, derivations, or first-principles predictions appear in the abstract or description. The central claims rest on experimental results rather than any reduction of outputs to inputs by construction, fitted parameters renamed as predictions, or load-bearing self-citations. This is a standard empirical methods paper with no detectable circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no specific free parameters, axioms, or invented entities can be identified from the text.

pith-pipeline@v0.9.1-grok · 5773 in / 1175 out tokens · 23600 ms · 2026-06-28T07:08:28.749581+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

28 extracted references · 2 linked inside Pith

[1]

seeing the big through the small

The msp-podcast corpus.arXiv preprint arXiv:2509.09791. Beiduo Chen, Siyao Peng, Anna Korhonen, and Bar- bara Plank. 2025. A rose by any other name: LLM- generated explanations are good proxies for human explanations to collect label distributions on NLI. In Findings of the Association for Computational Lin- guistics: ACL 2025, pages 10777–10802, Vienna, ...

arXiv 2025
[2]

InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4040–4054, Online

GoEmotions: A dataset of fine-grained emo- tions. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4040–4054, Online. Association for Computational Linguistics. Ali Pourramezan Fard, Mohammad Mehdi Hosseini, Timothy D Sweeny, and Mohammad H Mahoor. 2025. Affectnet+: A database for enhancing facial expres- si...

Pith/arXiv arXiv 2025
[3]

Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, and Hongfei Lin

Understanding r1-zero-like training: A critical perspective.Preprint, arXiv:2503.20783. Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, and Hongfei Lin

Pith/arXiv arXiv
[4]

InFindings of the As- sociation for Computational Linguistics: ACL 2025, pages 5609–5626

Is llm an overconfident judge? unveiling the capabilities of llms in detecting offensive language with annotation disagreement. InFindings of the As- sociation for Computational Linguistics: ACL 2025, pages 5609–5626. Johannes Mario Meissner, Napat Thumwanit, Saku Sug- awara, and Akiko Aizawa. 2021. Embracing ambi- guity: Shifting the training target of N...

arXiv 2025
[5]

A broad-coverage challenge corpus for sen- tence understanding through inference. InProceed- ings of the 2018 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112–1122. Association for Computational Linguistics. Jingyao Wu, Ting Dang, Vidhyasaharan Seth...

arXiv 2018
[6]

11 A Full GRPO Formulation For completeness, we provide the full formulation of GRPO, including the surrogate objective and importance-sampling formulation

Association for Computational Linguistics. 11 A Full GRPO Formulation For completeness, we provide the full formulation of GRPO, including the surrogate objective and importance-sampling formulation. Accordingly, for task m and sample q, GRPO samples a roll- out group G(m,q) of responses {o(m,q,i)}, where i∈G (m,q) indexes individual rollouts (i.e., a sam...

2020
[7]

entailment: the hypothesis logically follows from the premise
[8]

neutral: neither entailment nor contradiction can be determined
[9]

Output Con- straints Before outputting, check if the format of your output is in accordance with the requirements I provided

contradiction: the hypothesis conflicts with the premise You MUST produce a calibrated probability distribution. Output Con- straints Before outputting, check if the format of your output is in accordance with the requirements I provided
[10]

entailment

1. Generate the label probabilities in EXACTLY this JSON structure: {{"entailment": float, "neutral": float, "contradiction": float}}
[12]

Do not include any explanations or text besides the dictionary. D.4 Majority-label supervision (MLS) Baseline As a reference baseline, we first consider conven- tional Majority-label supervision (MLS), where the 15 Table 12: Prompt template used for MSP Podcast with example target utterance. Section Prompt Content BackgroundTwo speakers are having a conve...
[13]

Angry": float,

Generate the emotion probabilities in EXACTLY this JSON structure: {{"Angry": float, "Contempt": float, "Disgust": float, "Fear": float, "Happy": float, "Neu- tral": float, "Sad": float, "Surprise": float}}
[15]

proof" you said

Do not include any explanations or text besides the dictionary. annotator distribution is reduced to its majority la- bel: y∗ q = arg max c pq,c,(12) where pq,c denotes the annotator distribution de- fined in Eq. 2. Given the predicted distribution ˆp(q,i) defined in Eq. 3, the rollout reward is defined as the probabil- ity assigned to the majority label:...

2020
[16]

anger": float,

Generate the emotion probabilities in EXACTLY this JSON structure: {{"anger": float, "disgust": float, "fear": float, "joy": float, "sadness": float, "surprise": float, "neutral": float}}
[17]

The sum of all probabilities must be exactly 1.0
[18]

entailment

Do not include any explanations or text besides the dictionary. distribution-aware LLM alignment and ambiguity- aware learning approaches (Chen et al., 2025, 2024a; Lee et al., 2023). Additionally, we include prior ambiguity-aware fine-tuning methods such as (Zhou et al., 2022; Meissner et al., 2021), which are based on BERT- style architectures rather th...

2025
[19]

- This is the opposite of what the premise suggests

**Hypothesis Analysis**: - The hypothesis states that people got tired of data collection and clever analyses of what might happen. - This is the opposite of what the premise suggests
[20]

The premise suggests a shift towards data collection, while the hypothesis sug- gests a shift away from data collection

**Relationship Between Premise and Hypoth- esis**: - The premise and hypothesis are in di- rect contradiction. The premise suggests a shift towards data collection, while the hypothesis sug- gests a shift away from data collection
[21]

The premise and hypothesis are clearly in conflict

**Multiple Valid Interpretations**: - There are no multiple valid interpretations here. The premise and hypothesis are clearly in conflict
[22]

- The probabil- ity of neutrality is also 0 because the relationship is not neutral

**Probability Assignment**: - Since the premise and hypothesis are in direct contradiction, the probability of entailment is 0. - The probabil- ity of neutrality is also 0 because the relationship is not neutral. - The probability of contradiction is 1 because the hypothesis directly contradicts the premise. .00 .00 1.00 R1 The relationship between the pr...
[23]

This is a specific activity that involves creating a sandcastle

**Hypothesis Analysis**: - The hypothesis states that a child is building a sandcastle on the beach. This is a specific activity that involves creating a sandcastle
[24]

The child could be building a sandcastle, but they could also be doing other activities like digging, playing, or just standing in the sand

**Relationship Between Premise and Hypoth- esis**: - The premise does not provide enough information to confirm or deny the hypothesis. The child could be building a sandcastle, but they could also be doing other activities like digging, playing, or just standing in the sand
[25]

The child could be building a sandcastle, but they could also be doing other activities

**Multiple Valid Interpretations**: - There are multiple valid interpretations of the premise. The child could be building a sandcastle, but they could also be doing other activities. This lack of specificity in the premise means that the hy- pothesis is neither entailed nor contradicted by the premise
[26]

Linda Tripp was indicted for illegally taping telephone conversations with Monica Lewinsky

**Probability Assignment**: - Since the premise does not provide enough information to confirm or deny the hypothesis, the relationship between the two statements is neutral. The proba- bility of entailment is low because the premise does not logically follow the hypothesis. The probability of contradiction is also low because the premise does not conflic...
[27]

Monica Lewinsky was illegally taped by Linda Tripp

**Hypothesis Analysis**: - The hypothesis states that "Monica Lewinsky was illegally taped by Linda Tripp." - This suggests that Monica Lewinsky was the one who was taped by Linda Tripp
[28]

- The premise focuses on Linda Tripp as the tapper, while the hypothesis focuses on Monica Lewinsky as the taped person

**Relationship Between Premise and Hypothe- sis**: - The premise and hypothesis are describing the same event but from different perspectives. - The premise focuses on Linda Tripp as the tapper, while the hypothesis focuses on Monica Lewinsky as the taped person. - Since the premise explicitly states that Linda Tripp was the one who taped, the hypothesis ...
[29]

The premise and hypothesis are in direct conflict

**Multiple Valid Interpretations**: - There are no multiple valid interpretations here. The premise and hypothesis are in direct conflict
[30]

- The probability of the hypothesis being neutral or entailment is also 0 because the hypothesis directly contradicts the premise

**Probability Assignment**: - Given the di- rect contradiction between the premise and the hypothesis, the probability of the hypothesis being true given the premise is 0. - The probability of the hypothesis being neutral or entailment is also 0 because the hypothesis directly contradicts the premise. .00 .00 1.00 R1 The relationship between the premise a...

[1] [1]

seeing the big through the small

The msp-podcast corpus.arXiv preprint arXiv:2509.09791. Beiduo Chen, Siyao Peng, Anna Korhonen, and Bar- bara Plank. 2025. A rose by any other name: LLM- generated explanations are good proxies for human explanations to collect label distributions on NLI. In Findings of the Association for Computational Lin- guistics: ACL 2025, pages 10777–10802, Vienna, ...

arXiv 2025

[2] [2]

InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4040–4054, Online

GoEmotions: A dataset of fine-grained emo- tions. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4040–4054, Online. Association for Computational Linguistics. Ali Pourramezan Fard, Mohammad Mehdi Hosseini, Timothy D Sweeny, and Mohammad H Mahoor. 2025. Affectnet+: A database for enhancing facial expres- si...

Pith/arXiv arXiv 2025

[3] [3]

Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, and Hongfei Lin

Understanding r1-zero-like training: A critical perspective.Preprint, arXiv:2503.20783. Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, and Hongfei Lin

Pith/arXiv arXiv

[4] [4]

InFindings of the As- sociation for Computational Linguistics: ACL 2025, pages 5609–5626

Is llm an overconfident judge? unveiling the capabilities of llms in detecting offensive language with annotation disagreement. InFindings of the As- sociation for Computational Linguistics: ACL 2025, pages 5609–5626. Johannes Mario Meissner, Napat Thumwanit, Saku Sug- awara, and Akiko Aizawa. 2021. Embracing ambi- guity: Shifting the training target of N...

arXiv 2025

[5] [5]

A broad-coverage challenge corpus for sen- tence understanding through inference. InProceed- ings of the 2018 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112–1122. Association for Computational Linguistics. Jingyao Wu, Ting Dang, Vidhyasaharan Seth...

arXiv 2018

[6] [6]

11 A Full GRPO Formulation For completeness, we provide the full formulation of GRPO, including the surrogate objective and importance-sampling formulation

Association for Computational Linguistics. 11 A Full GRPO Formulation For completeness, we provide the full formulation of GRPO, including the surrogate objective and importance-sampling formulation. Accordingly, for task m and sample q, GRPO samples a roll- out group G(m,q) of responses {o(m,q,i)}, where i∈G (m,q) indexes individual rollouts (i.e., a sam...

2020

[7] [7]

entailment: the hypothesis logically follows from the premise

[8] [8]

neutral: neither entailment nor contradiction can be determined

[9] [9]

Output Con- straints Before outputting, check if the format of your output is in accordance with the requirements I provided

contradiction: the hypothesis conflicts with the premise You MUST produce a calibrated probability distribution. Output Con- straints Before outputting, check if the format of your output is in accordance with the requirements I provided

[10] [10]

entailment

1. Generate the label probabilities in EXACTLY this JSON structure: {{"entailment": float, "neutral": float, "contradiction": float}}

[11] [12]

Do not include any explanations or text besides the dictionary. D.4 Majority-label supervision (MLS) Baseline As a reference baseline, we first consider conven- tional Majority-label supervision (MLS), where the 15 Table 12: Prompt template used for MSP Podcast with example target utterance. Section Prompt Content BackgroundTwo speakers are having a conve...

[12] [13]

Angry": float,

Generate the emotion probabilities in EXACTLY this JSON structure: {{"Angry": float, "Contempt": float, "Disgust": float, "Fear": float, "Happy": float, "Neu- tral": float, "Sad": float, "Surprise": float}}

[13] [15]

proof" you said

Do not include any explanations or text besides the dictionary. annotator distribution is reduced to its majority la- bel: y∗ q = arg max c pq,c,(12) where pq,c denotes the annotator distribution de- fined in Eq. 2. Given the predicted distribution ˆp(q,i) defined in Eq. 3, the rollout reward is defined as the probabil- ity assigned to the majority label:...

2020

[14] [16]

anger": float,

Generate the emotion probabilities in EXACTLY this JSON structure: {{"anger": float, "disgust": float, "fear": float, "joy": float, "sadness": float, "surprise": float, "neutral": float}}

[15] [17]

The sum of all probabilities must be exactly 1.0

[16] [18]

entailment

Do not include any explanations or text besides the dictionary. distribution-aware LLM alignment and ambiguity- aware learning approaches (Chen et al., 2025, 2024a; Lee et al., 2023). Additionally, we include prior ambiguity-aware fine-tuning methods such as (Zhou et al., 2022; Meissner et al., 2021), which are based on BERT- style architectures rather th...

2025

[17] [19]

- This is the opposite of what the premise suggests

**Hypothesis Analysis**: - The hypothesis states that people got tired of data collection and clever analyses of what might happen. - This is the opposite of what the premise suggests

[18] [20]

The premise suggests a shift towards data collection, while the hypothesis sug- gests a shift away from data collection

**Relationship Between Premise and Hypoth- esis**: - The premise and hypothesis are in di- rect contradiction. The premise suggests a shift towards data collection, while the hypothesis sug- gests a shift away from data collection

[19] [21]

The premise and hypothesis are clearly in conflict

**Multiple Valid Interpretations**: - There are no multiple valid interpretations here. The premise and hypothesis are clearly in conflict

[20] [22]

- The probabil- ity of neutrality is also 0 because the relationship is not neutral

**Probability Assignment**: - Since the premise and hypothesis are in direct contradiction, the probability of entailment is 0. - The probabil- ity of neutrality is also 0 because the relationship is not neutral. - The probability of contradiction is 1 because the hypothesis directly contradicts the premise. .00 .00 1.00 R1 The relationship between the pr...

[21] [23]

This is a specific activity that involves creating a sandcastle

**Hypothesis Analysis**: - The hypothesis states that a child is building a sandcastle on the beach. This is a specific activity that involves creating a sandcastle

[22] [24]

The child could be building a sandcastle, but they could also be doing other activities like digging, playing, or just standing in the sand

**Relationship Between Premise and Hypoth- esis**: - The premise does not provide enough information to confirm or deny the hypothesis. The child could be building a sandcastle, but they could also be doing other activities like digging, playing, or just standing in the sand

[23] [25]

The child could be building a sandcastle, but they could also be doing other activities

**Multiple Valid Interpretations**: - There are multiple valid interpretations of the premise. The child could be building a sandcastle, but they could also be doing other activities. This lack of specificity in the premise means that the hy- pothesis is neither entailed nor contradicted by the premise

[24] [26]

Linda Tripp was indicted for illegally taping telephone conversations with Monica Lewinsky

**Probability Assignment**: - Since the premise does not provide enough information to confirm or deny the hypothesis, the relationship between the two statements is neutral. The proba- bility of entailment is low because the premise does not logically follow the hypothesis. The probability of contradiction is also low because the premise does not conflic...

[25] [27]

Monica Lewinsky was illegally taped by Linda Tripp

**Hypothesis Analysis**: - The hypothesis states that "Monica Lewinsky was illegally taped by Linda Tripp." - This suggests that Monica Lewinsky was the one who was taped by Linda Tripp

[26] [28]

- The premise focuses on Linda Tripp as the tapper, while the hypothesis focuses on Monica Lewinsky as the taped person

**Relationship Between Premise and Hypothe- sis**: - The premise and hypothesis are describing the same event but from different perspectives. - The premise focuses on Linda Tripp as the tapper, while the hypothesis focuses on Monica Lewinsky as the taped person. - Since the premise explicitly states that Linda Tripp was the one who taped, the hypothesis ...

[27] [29]

The premise and hypothesis are in direct conflict

**Multiple Valid Interpretations**: - There are no multiple valid interpretations here. The premise and hypothesis are in direct conflict

[28] [30]

- The probability of the hypothesis being neutral or entailment is also 0 because the hypothesis directly contradicts the premise

**Probability Assignment**: - Given the di- rect contradiction between the premise and the hypothesis, the probability of the hypothesis being true given the premise is 0. - The probability of the hypothesis being neutral or entailment is also 0 because the hypothesis directly contradicts the premise. .00 .00 1.00 R1 The relationship between the premise a...