Papers by Alexis Ross
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2024.naacl-long)
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Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim
| Challenge: | Recent language models possess impressive performance across a wide range of tasks . however, they often rely on narrow, non-transferable procedures for task-solving . |
| Approach: | They propose to evaluate language models using "counterfactual" task variants that deviate from standard tasks. |
| Outcome: | The proposed framework shows that language models perform better on a wide range of tasks compared to the default conditions. |
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews (2024.acl-long)
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| Challenge: | Existing systems that can interpret complex writing feedback and edit documents in response are limited on the most demanding writing tasks. |
| Approach: | They propose to use peer feedback to revise scientific papers based on peer feedback . they provide labels linking each reviewer comment to the specific paper edits made by the author . |
| Outcome: | The proposed model fails to identify which edits correspond to a comment and the original paper. |
How well do NLI models capture verb veridicality? (D19-1)
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| Challenge: | In natural language inference, contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world. |
| Approach: | They propose to use a dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs. |
| Outcome: | The proposed model learns to make correct inferences about veridicality in verb-complement constructions. |
Does Self-Rationalization Improve Robustness to Spurious Correlations? (2022.emnlp-main)
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| Challenge: | Rationalization is fundamental to human reasoning and learning. |
| Approach: | They evaluate robustness to spurious correlations in encoder-decoder and decoder-only models . authors say explanations can come at the cost of robustness . |
| Outcome: | The proposed model outputs are more interpretable and easier to interact with for end-users than nonrationalizing models. |
Toward In-Context Teaching: Adapting Examples to Students’ Misconceptions (2024.acl-long)
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| Challenge: | Using computational models as pedagogical tools is becoming increasingly popular, but how effective can these models adapt as teachers to students of different types? |
| Approach: | They propose a suite of models and evaluation methods that combine Bayesian student models and AToM to evaluate adaptive teaching methods. |
| Outcome: | The proposed models outperform LLM-based and standard Bayesian teaching methods in the evaluation of simulated students across three learning domains. |
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation (2023.acl-long)
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| Challenge: | Existing methods for analyzing and training NLP models have not been integrated to combine their complementary advantages. |
| Approach: | They introduce a framework for selective rationalization and counterfactual text generation that leverages CREST to regularize selective rationales and a loss function that regularizes selective rationals. |
| Outcome: | The proposed framework generates valid counterfactuals that are more natural than those produced by previous methods and can be used for data augmentation at scale. |
Language Modeling with Editable External Knowledge (2025.findings-naacl)
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| Challenge: | Continual learning methods tackle the problem of a changing world by incrementally training on new information. |
| Approach: | They propose a retrieval-augmented generation approach that incrementally deletes or rewrites other entries in the knowledge base each time a document is added. |
| Outcome: | The proposed model improves accuracy relative to conventional retrieval-augmented generation by 7-13% and 6-10% absolute. |
Competency Problems: On Finding and Removing Artifacts in Language Data (2021.emnlp-main)
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Matt Gardner, William Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh, Noah A. Smith
| Challenge: | Recent work in NLP has documented dataset artifacts, bias, and spurious correlations . how to tell which features have spurious instead of legitimate correlations is typically left unspecified . |
| Approach: | They propose a class of competency problems to formalize this notion into a classification . they show that realistic datasets will increasingly deviate from competency problems . |
| Outcome: | The proposed model can be used to show that models are inappropriately affected by these less extreme biases. |
Tailor: Generating and Perturbing Text with Semantic Controls (2022.acl-long)
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| Challenge: | Existing studies train task-specific generators, relying on training a model for every perturbation. |
| Approach: | They propose a semantically-controlled text generation system that modifies sentences to match target attributes. |
| Outcome: | The proposed system produces textual outputs conditioned on control codes derived from semantic representations. |
Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2021.findings-acl)
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| Challenge: | Cognitive science and philosophy research has shown that human explanations are contrastive . a contrast case plays a key role in modulating what explanations can be given . |
| Approach: | They propose a method for producing contrastive explanations of model predictions . they edit models' outputs to change model outputs, and then edit them to the contrast case . |
| Outcome: | a new method produces contrastive explanations of model predictions in the form of edits . the edits are minimal and fluent, consistent with human contrastive edits. |
Scaling Collaborative Effort with Agents (2026.findings-acl)
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Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J Shen, Ameet Talwalkar, Tongshuang Wu, David Sontag
| Challenge: | Current evaluations of agents focus on producing high-quality, final outputs in one shot, failing to account for the inherently iterative nature of many real-world problems. |
| Approach: | They propose a framework that captures how an agent’s utility grows with increasing user involvement. |
| Outcome: | The proposed framework captures how an agent’s utility grows with increasing user involvement, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. |