Papers by Peter West
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)
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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi
| Challenge: | Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited. |
| Approach: | They propose an inference-time policy adapter which tailors a large base model without fine-tuning it. |
| Outcome: | The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4. |
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2021.naacl-main)
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| Challenge: | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. |
| Approach: | They propose an algorithm that enables neural language models to generate fluent text while satisfying complex lexical constraints. |
| Outcome: | The proposed algorithm outperforms existing methods on four benchmarks and shows that it handles any set of lexical constraints expressible under predicate logic while its asymptotic runtime is equivalent to conventional beam search. |
Unsupervised Commonsense Question Answering with Self-Talk (2020.emnlp-main)
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| Challenge: | Current systems rely on pre-trained language models or external knowledge bases to incorporate additional relevant knowledge. |
| Approach: | They propose an unsupervised framework based on self-talk to improve commonsense performance by asking language models to ask information seeking questions. |
| Outcome: | Empirical results show that the proposed framework improves on four out of six commonsense benchmarks and competes with models that obtain knowledge from external KBs. |
Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model (2024.naacl-long)
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Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi
| Challenge: | Impossible Distillation is a framework for paraphrasing and sentence summarization that can be trained from a low-quality teacher model. |
| Approach: | They propose a framework that distills a high-quality dataset from a low-quality teacher . they hypothesize and verify the paraphrastic proximity intrinsic to pre-trained LMs . |
| Outcome: | The proposed framework outperforms baseline models on unconstrained paraphrase generation and sentence summarization benchmarks. |
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models (2021.acl-long)
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| Challenge: | Existing methods for generating text are unsupervised and require supervision. |
| Approach: | They propose an unsupervised method that uses two off-the-shelf pretrained LMs in opposite directions to apply them to non-sequential tasks. |
| Outcome: | The proposed method outperforms strong unsupervised baselines on paraphrasing and abductive text infilling. |
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)
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| Challenge: | Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision. |
| Approach: | They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle. |
| Outcome: | The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes. |
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation (2023.findings-emnlp)
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Peter West, Ronan Bras, Taylor Sorensen, Bill Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi
| Challenge: | a new commonsense knowledge model, NovaCOMET, combines knowledge and general task models. |
| Approach: | They propose an open commonsense knowledge model that combines knowledge and general task models. |
| Outcome: | The proposed model matches or exceeds existing knowledge models on commonsense reasoning tasks. |
Probing Factually Grounded Content Transfer with Factual Ablation (2022.findings-acl)
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| Challenge: | Despite recent success, large neural models often generate factually incorrect text . lack of a standard evaluation for factuality complicates factual grounded generation . |
| Approach: | They propose a method to measure factual consistency by presenting two evaluation sets . large pretrained models have shown impressive effectiveness at longstanding tasks . |
| Outcome: | The proposed method improves over strong baselines by presenting two evaluation sets. |
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation (2023.acl-long)
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Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
| Challenge: | Empirical results suggest that scale is not the only way to build commonsense capabilities. |
| Approach: | They propose a commonsense distillation framework that can achieve a competitive level of commonsensing without relying on the benefits of scale. |
| Outcome: | The proposed framework breaks the dependence on the extreme-scale teacher model with two innovations: (1) the novel adaptation of NeuroLogic Decoding to enhance the generation quality of the weak, off-the-shelf language models, and (2) self-imitation learning to iteratively learn from the model’s own enhanced commonsense acquisition capabilities. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
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Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization (2023.emnlp-main)
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Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin Choi
| Challenge: | a dataset of 1.5 million conversations distilled from everyday spoken situations is limited in scale due to its associated costs. |
| Approach: | They propose to make SODA a publicly available, million-scale high-quality social dialogue dataset . they contextualize social commonsense knowledge from a knowledge graph to distill broad spectrum of social interactions . |
| Outcome: | The proposed dataset is the first publicly available, million-scale high-quality social dialogue dataset. |
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning (2020.emnlp-main)
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Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi
| Challenge: | Existing methods for integrating past and future contexts are limited and require manual input. |
| Approach: | They propose an unsupervised decoding algorithm that incorporates past and future contexts using off-the-shelf, left-to-right language models and no supervision. |
| Outcome: | The proposed method outperforms unsupervised methods on abductive and counterfactual reasoning tasks. |
Minding Language Models’ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker (2023.acl-long)
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| Challenge: | Empirical results show plug-and-play approach to reason about belief states of multiple characters in reading comprehension tasks is more precise and interpretable than previous approaches. |
| Approach: | They propose a plug-and-play approach to reason about the belief states of multiple characters in reading comprehension tasks via explicit symbolic representation. |
| Outcome: | The proposed algorithm improves theory of mind of off-the-shelf neural language models without supervision. |
Infusing Theory of Mind into Socially Intelligent LLM Agents (2026.findings-acl)
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| Challenge: | Theory of Mind (ToM) is a key aspect of human social intelligence, yet chatbots and LLMs do not typically integrate it. |
| Approach: | They propose a method that integrates Theory of Mind (ToM) into chatbots and dialogue agents to generate mental states between dialogue turns. |
| Outcome: | The proposed method improves dialogue and social interaction by integrating ToM with dialogue lookahead. |
We’re Afraid Language Models Aren’t Modeling Ambiguity (2023.emnlp-main)
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Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, Yejin Choi
| Challenge: | Ambiguity is an intrinsic feature of natural language, allowing us to anticipate misunderstandings and revise our interpretations as listeners. |
| Approach: | They use AmbiEnt to capture ambiguity in a sentence and analyze it to evaluate pretrained LMs. |
| Outcome: | The proposed model can flag political claims in the wild that are misleading due to ambiguity. |
Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right (2021.emnlp-main)
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| Challenge: | Large language models have shown promising results in zero-shot settings due to surface form competition . since probability mass is finite, this lowers the probability of the correct answer . |
| Approach: | They propose a scoring function that compensates for surface form competition by reweighing each option according to its a priori likelihood. |
| Outcome: | The proposed scoring function achieves consistent gains in zero-shot over calibrated and uncalibrated scoring functions on all GPT-2 and GPT-3 models on a variety of multiple choice datasets. |
BottleHumor: Self-Informed Humor Explanation using the Information Bottleneck Principle (2025.findings-acl)
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| Challenge: | Humor is an effective communication tool that can manifest in various forms, including puns, exaggerated facial expressions, absurd behaviors, and incongruities. |
| Approach: | They propose a method that elicits relevant world knowledge from vision and language models and refines it to generate an explanation of the humor in an unsupervised manner. |
| Outcome: | The proposed method can be adapted for additional tasks that can benefit from eliciting and conditioning on relevant world knowledge. |
Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation (2022.emnlp-main)
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| Challenge: | a new framework for sentence summarization is available that can be trained reference-free . a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios is obtained . |
| Approach: | They propose a framework for sentence summarization that can be trained reference-free . they propose 'referee' that iteratively distills latent knowledge into better models . |
| Outcome: | The proposed framework outperforms existing models in the use of explicit examples from teacher models without compromising the quality of the summarization. |
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2022.naacl-main)
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Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi
| Challenge: | Prior studies suggested pre-trained language models possess limited understanding of commonsense knowledge despite otherwise stellar performance on leaderboards. |
| Approach: | They propose a framework that uses larger models to teach smaller models by distilling knowledge symbolically as text in addition to the neural model. |
| Outcome: | The proposed framework is based on a general language model teacher's commonsense knowledge graphs and a neural commonsensing model surpassing the teacher model's in all three criteria. |
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2022.naacl-main)
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Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi
| Challenge: | Existing paradigms for text generation are left-to-right decoding from autoregressive language models. |
| Approach: | They propose a decoding algorithm that incorporates heuristic estimates of future cost that are efficient for large-scale language models. |
| Outcome: | The proposed method outperforms baselines on five generation tasks and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. |