Paper Citation Record · LEDGER

Neural Text Generation with Unlikelihood Training

As of 22 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 44 inbound Pith citation observations for arXiv:1908.04319.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.04319 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:50:29.118909Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 44 of 44 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:19:39.664378Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T19:40:07.174377Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy3
unresolved15
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f764b3f-586d-4581-974c-754c8d2f732c · outbound

This paper cites Negative Training for Neural Dialogue Response Generation.

Neural Text Generation with Unlikelihood Training Negative Training for Neural Dialogue Response Generation

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T13:50:29.877827Z

Source-reported events for the cited work

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Observation 5bd5e94e-0f50-4d85-8da3-cdab3288faa5 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Neural Text Generation with Unlikelihood Training The Curious Case of Neural Text Degeneration

Reference 6

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Observation dbd9d370-29e2-4f33-b627-2735f319b2c5 · outbound

This paper cites OpenNMT: Open-Source Toolkit for Neural Machine Translation.

Neural Text Generation with Unlikelihood Training OpenNMT: Open-Source Toolkit for Neural Machine Translation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:28.949929Z

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Observation 538a0621-7a88-4245-86d1-2e50a9f2d69e · outbound

This paper cites Importance of Search and Evaluation Strategies in Neural Dialogue Modeling.

Neural Text Generation with Unlikelihood Training Importance of Search and Evaluation Strategies in Neural Dialogue Modeling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:28.963220Z

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Observation 3b350f41-df1d-4a9e-aab0-c7b24fc8291d · outbound

This paper cites ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons.

Neural Text Generation with Unlikelihood Training ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:28.993949Z

Source-reported events for the cited work

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Observation a4574f50-cc15-402f-a499-d78b7752d8c9 · outbound

This paper cites Pointer Sentinel Mixture Models.

Neural Text Generation with Unlikelihood Training Pointer Sentinel Mixture Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:29.006137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cecf28c-d096-4371-ab39-2ca505dcb2e7 · outbound

This paper cites In Proceedings of NAACL-HLT 2019: Demonstrations.

Neural Text Generation with Unlikelihood Training In Proceedings of NAACL-HLT 2019: Demonstrations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:50:30.198074Z

Source-reported events for the cited work

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Observation ff479cc2-daf1-480f-8a8c-ed7c5dc0e60c · outbound

This paper cites A Deep Reinforced Model for Abstractive Summarization.

Neural Text Generation with Unlikelihood Training A Deep Reinforced Model for Abstractive Summarization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:29.032243Z

Source-reported events for the cited work

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Observation b1315ab9-0c26-471f-8309-629163d07a57 · outbound

This paper cites an unresolved cited work.

Neural Text Generation with Unlikelihood Training Unresolved cited work

Reference 15

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Source-reported events for the cited work

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Observation 66ad89b0-7426-4908-9bc9-5df67b17cf70 · outbound

This paper cites Minimum Risk Training for Neural Machine Translation.

Neural Text Generation with Unlikelihood Training Minimum Risk Training for Neural Machine Translation

Reference 16

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Source-reported events for the cited work

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Observation a0168a79-abb7-4e2f-87e2-bdc3a2f7cc73 · outbound

This paper cites Retrieve and Refine: Improved Sequence Generation Models For Dialogue.

Neural Text Generation with Unlikelihood Training Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:29.079588Z

Source-reported events for the cited work

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Observation ac265774-64d3-485b-8623-c70e839a6403 · outbound

This paper cites SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient.

Neural Text Generation with Unlikelihood Training SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:29.091868Z

Source-reported events for the cited work

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Observation ddf5ccfb-d3bc-4237-82af-1e542f6f687e · outbound

This paper cites Association for Computational Linguistics.

Neural Text Generation with Unlikelihood Training Association for Computational Linguistics

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:50:30.122801Z

Source-reported events for the cited work

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Observation ca49a71b-660e-42a3-a64a-b0a4b1357aa4 · outbound

This paper cites 41% for LUL-seq greedy, 73% for LUL-tok+seq greedy), though using n-gram repetition candidates yielded further improvements (§5.2, Table 5).

Neural Text Generation with Unlikelihood Training 41% for LUL-seq greedy, 73% for LUL-tok+seq greedy), though using n-gram repetition candidates yielded further improvements (§5.2, Table 5)

Reference 20

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Source-reported events for the cited work

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Observation e2b7b287-d29d-4e7c-8d2f-fb744ac723fd · outbound

This paper cites In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP.

Neural Text Generation with Unlikelihood Training In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP

Reference 2002

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:50:30.236980Z

Source-reported events for the cited work

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Observation 9e49b7fb-e654-40b0-8881-310ca99a8fbc · outbound

This paper cites Sequence Level Training with Recurrent Neural Networks.

Neural Text Generation with Unlikelihood Training Sequence Level Training with Recurrent Neural Networks

Reference 2015

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Source-reported events for the cited work

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Observation fe420cef-b1f3-434b-9c1a-2804f4f149ed · outbound

This paper cites A Simple, Fast Diverse Decoding Algorithm for Neural Generation.

Neural Text Generation with Unlikelihood Training A Simple, Fast Diverse Decoding Algorithm for Neural Generation

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:28.977491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 277f5d67-988d-452d-aeef-dd4c329c6ce4 · outbound

This paper cites Classical Structured Prediction Losses for Sequence to Sequence Learning.

Neural Text Generation with Unlikelihood Training Classical Structured Prediction Losses for Sequence to Sequence Learning

Reference 2017

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f43d2b3f-dab5-4e52-a916-1dfcf59c52e0 · outbound

This paper cites Hierarchical Neural Story Generation.

Neural Text Generation with Unlikelihood Training Hierarchical Neural Story Generation

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:28.915045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6b6f61-92e3-42ee-b5d3-3ea41358f489 · outbound

This paper cites The Second Conversational Intelligence Challenge (ConvAI2).

Neural Text Generation with Unlikelihood Training The Second Conversational Intelligence Challenge (ConvAI2)

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-14T13:50:28.895411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation efe6223b-71fc-4a72-b8ee-f45c9cc2ba17 · inbound

Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning cites this paper.

Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning Neural Text Generation with Unlikelihood Training

Reference 53

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Observation 4b3e41b8-75a8-436d-b219-63dbc927e9c3 · inbound

CTRL: A Conditional Transformer Language Model for Controllable Generation cites this paper.

CTRL: A Conditional Transformer Language Model for Controllable Generation Neural Text Generation with Unlikelihood Training

Reference 48

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Observation 73037089-7a32-49c1-aad8-8c7110ea4d36 · inbound

Learning to summarize from human feedback cites this paper.

Learning to summarize from human feedback Neural Text Generation with Unlikelihood Training

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:46:18.679129Z

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Observation db1809e2-df57-4895-b7e7-9cecaf1c03d5 · inbound

Direct Preference Optimization: Your Language Model is Secretly a Reward Model cites this paper.

Direct Preference Optimization: Your Language Model is Secretly a Reward Model Neural Text Generation with Unlikelihood Training

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:33:28.304766Z

Source-reported events for the cited work

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Observation c95a52db-fc14-43e6-b792-a57acb23f7e2 · inbound

Aligning Modalities in Vision Large Language Models via Preference Fine-tuning cites this paper.

Aligning Modalities in Vision Large Language Models via Preference Fine-tuning Neural Text Generation with Unlikelihood Training

Reference 76

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Observation db259ed8-6ac3-4e39-8997-7e99f524df16 · inbound

ORPO: Monolithic Preference Optimization without Reference Model cites this paper.

ORPO: Monolithic Preference Optimization without Reference Model Neural Text Generation with Unlikelihood Training

Reference 136

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Source-reported events for the cited work

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Observation e1ad950f-bcb4-4a33-966a-645f1a39bf38 · inbound

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents cites this paper.

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents Neural Text Generation with Unlikelihood Training

Reference 158

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 14c4a7db-a81f-4dd5-9543-93fac1c95f89 · inbound

Adaptive Decoding via Latent Preference Optimization cites this paper.

Adaptive Decoding via Latent Preference Optimization Neural Text Generation with Unlikelihood Training

Reference 26

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Observation 7300f804-e1c9-4676-a2f6-a98f11feb38d · inbound

Mitigating Knowledge Conflicts in Language Model-Driven Question Answering cites this paper.

Mitigating Knowledge Conflicts in Language Model-Driven Question Answering Neural Text Generation with Unlikelihood Training

Reference 8

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Observation 07e9fdc8-14a3-427e-8b59-0907adeadaa7 · inbound

Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning cites this paper.

Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning Neural Text Generation with Unlikelihood Training

Reference 36

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Observation 8e7908ff-3a34-4f41-b1b7-9c24926ba89e · inbound

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation cites this paper.

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation Neural Text Generation with Unlikelihood Training

Reference 28

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Observation 9858d033-6fb1-43ed-905d-22e315103c9f · inbound

LCFO: Long Context and Long Form Output Dataset and Benchmarking cites this paper.

LCFO: Long Context and Long Form Output Dataset and Benchmarking Neural Text Generation with Unlikelihood Training

Reference 28

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Observation dd284e27-c5d0-4f09-ac20-e3157824f2ac · inbound

Large Concept Models: Language Modeling in a Sentence Representation Space cites this paper.

Large Concept Models: Language Modeling in a Sentence Representation Space Neural Text Generation with Unlikelihood Training

Reference 118

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FaGeL: Fabric LLMs Agent empowered Embodied Intelligence Evolution with Autonomous Human-Machine Collaboration cites this paper.

FaGeL: Fabric LLMs Agent empowered Embodied Intelligence Evolution with Autonomous Human-Machine Collaboration Neural Text Generation with Unlikelihood Training

Reference 33

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Observation eb4b95a4-f136-4230-afab-22891498d989 · inbound

A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy cites this paper.

A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy Neural Text Generation with Unlikelihood Training

Reference 211

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Observation 22cb6a84-429b-4629-8e5f-f2c52eb3682b · inbound

The Differences Between Direct Alignment Algorithms are a Blur cites this paper.

The Differences Between Direct Alignment Algorithms are a Blur Neural Text Generation with Unlikelihood Training

Reference 41

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Observation 46140101-91c8-45d6-861d-76a58151666e · inbound

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models cites this paper.

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models Neural Text Generation with Unlikelihood Training

Reference 22

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Observation 49b76e98-067e-4da8-a2cb-16981ed4cdd8 · inbound

Context-Enhanced Contrastive Search for Improved LLM Text Generation cites this paper.

Context-Enhanced Contrastive Search for Improved LLM Text Generation Neural Text Generation with Unlikelihood Training

Reference 12

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Observation 3719482d-9228-4b9c-a973-3f5f38f008fa · inbound

Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration cites this paper.

Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Neural Text Generation with Unlikelihood Training

Reference 13

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Observation e830fb53-78a8-4b95-98e4-08ef26c4f7a0 · inbound

GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval cites this paper.

GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval Neural Text Generation with Unlikelihood Training

Reference 30

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Observation d2209df2-7498-4ed4-8c9d-23ce6917a6b3 · inbound

Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation cites this paper.

Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation Neural Text Generation with Unlikelihood Training

Reference 70

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Observation 3944346b-d07f-4888-b30a-eef52552b6c0 · inbound

On the Fitness Landscape in the $NK$ Model cites this paper.

On the Fitness Landscape in the $NK$ Model Neural Text Generation with Unlikelihood Training

Reference 66

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Observation c236ebda-f6c7-4a5b-a2d2-a25e06254ba0 · inbound

Avoidance Decoding for Diverse Multi-Branch Story Generation cites this paper.

Avoidance Decoding for Diverse Multi-Branch Story Generation Neural Text Generation with Unlikelihood Training

Reference 39

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Observation 9a6c2e49-c071-4832-b800-28a78a1a956e · inbound

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model cites this paper.

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model Neural Text Generation with Unlikelihood Training

Reference 31

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Observation e527a874-e97d-4d94-bd91-8784ee2e8a31 · inbound

A Universal Avoidance Method for Diverse Multi-branch Generation cites this paper.

A Universal Avoidance Method for Diverse Multi-branch Generation Neural Text Generation with Unlikelihood Training

Reference 29

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Observation 667a479c-7385-4728-9831-f4dcab69aaaa · inbound

Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback cites this paper.

Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback Neural Text Generation with Unlikelihood Training

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:50:54.392111Z

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Observation 5a6b95d2-3897-4e3a-b717-2d226bb7c158 · inbound

Annotations Mitigate Post-Training Mode Collapse cites this paper.

Annotations Mitigate Post-Training Mode Collapse Neural Text Generation with Unlikelihood Training

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:46:51.960967Z

Source-reported events for the cited work

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Observation fd4e5731-cdc1-4c16-adc7-c414c16a397f · inbound

Self-Attention as a Covariance Readout: A Unified View of In-Context Learning and Repetition cites this paper.

Self-Attention as a Covariance Readout: A Unified View of In-Context Learning and Repetition Neural Text Generation with Unlikelihood Training

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:46:29.488804Z

Source-reported events for the cited work

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Observation 46cd232d-d897-4723-a869-b7ff49898b6c · inbound

SOMA: Efficient Multi-turn LLM Serving via Small Language Model cites this paper.

SOMA: Efficient Multi-turn LLM Serving via Small Language Model Neural Text Generation with Unlikelihood Training

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:42:04.262872Z

Source-reported events for the cited work

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Observation fc5257cb-2a25-448f-97c2-9fad8d5b5759 · inbound

Asking Back: Interaction-Layer Antidistillation Watermarks cites this paper.

Asking Back: Interaction-Layer Antidistillation Watermarks Neural Text Generation with Unlikelihood Training

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-20T18:13:37.544601Z

Source-reported events for the cited work

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Observation 421645f1-8c4f-4f5e-ae2a-4734beba09e2 · inbound

CLORE: Content-Level Optimization for Reasoning Efficiency cites this paper.

CLORE: Content-Level Optimization for Reasoning Efficiency Neural Text Generation with Unlikelihood Training

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:51:08.106790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 71df3d1d-1390-4b58-bf11-4d841904de37 · inbound

AMix-2: Establishing Protein as a Native Modality in Large Language Models cites this paper.

AMix-2: Establishing Protein as a Native Modality in Large Language Models Neural Text Generation with Unlikelihood Training

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-28T20:12:37.772843Z

Source-reported events for the cited work

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Observation e2a7de01-2aeb-48c9-b1a5-6bd15dc055e1 · inbound

Seeing the Hivemind: A Consensus-Aware Interaction Technique for Mitigating AI Homogenization cites this paper.

Seeing the Hivemind: A Consensus-Aware Interaction Technique for Mitigating AI Homogenization Neural Text Generation with Unlikelihood Training

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-03T03:37:35.947111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 033f5f21-3979-425f-98f5-f42c7521ea91 · inbound

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation cites this paper.

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation Neural Text Generation with Unlikelihood Training

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.175970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cb53792a-9591-4018-8b55-523eeede3c9a · inbound

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation cites this paper.

Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation Neural Text Generation with Unlikelihood Training

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:35:39.593537Z

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Observation 9f5819d0-170d-4072-81db-f5a8abed1e35 · inbound

Mitigating Package Hallucinations in Large Language Models via Model Editing cites this paper.

Mitigating Package Hallucinations in Large Language Models via Model Editing Neural Text Generation with Unlikelihood Training

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:57:47.298927Z

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Observation bfb19622-cd95-4af0-a83a-c164290a885c · inbound

MentalThink: Shaping Thoughts in Mental SVG World cites this paper.

MentalThink: Shaping Thoughts in Mental SVG World Neural Text Generation with Unlikelihood Training

Reference 242

Resolution: unresolved; no resolver link, observed 2026-07-12T01:50:59.184754Z

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Observation b2c42370-35df-4c45-94cc-4cc272e3d3d0 · inbound

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure cites this paper.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Neural Text Generation with Unlikelihood Training

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-11T14:23:31.109787Z

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Observation b8f00bbc-86a1-4398-906c-23b5739303d6 · inbound

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure cites this paper.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Neural Text Generation with Unlikelihood Training

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T08:38:37.781693Z

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Observation 77cfbb00-f862-4196-adfc-dc88b6390736 · inbound

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift cites this paper.

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift Neural Text Generation with Unlikelihood Training

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T17:47:42.511417Z

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Observation 157afa55-0b54-448c-bc60-f3703568036b · inbound

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS cites this paper.

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS Neural Text Generation with Unlikelihood Training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T09:46:58.789149Z

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Observation 579802b6-8b4c-4597-ad9e-7114a2e88dcc · inbound

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS cites this paper.

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS Neural Text Generation with Unlikelihood Training

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T02:07:32.345073Z

Source-reported events for the cited work

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Observation 3c8182a4-d9b0-452a-9499-e701db41df60 · inbound

Bayesian Repetition Penalty: A Principled Adjacent-Conditional Framework for Reversing Attention Collapse in Autoregressive Language Models cites this paper.

Bayesian Repetition Penalty: A Principled Adjacent-Conditional Framework for Reversing Attention Collapse in Autoregressive Language Models Neural Text Generation with Unlikelihood Training

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T21:23:34.561927Z

Source-reported events for the cited work

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Observation 1d9a606e-1eff-40e2-b66f-a52a365ea626 · inbound

Verifiably grounded machine interpretation of lunar geology cites this paper.

Verifiably grounded machine interpretation of lunar geology Neural Text Generation with Unlikelihood Training

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T20:26:57.703701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.