Papers by Alex Peng
BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2020.tacl-1)
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Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, Samuel R. Bowman
| Challenge: | Recent studies have examined how linguistic knowledge of language models (LMs) varies across English phenomena. |
| Approach: | They propose a benchmark to evaluate linguistic knowledge of language models on major grammatical phenomena in English. |
| Outcome: | The proposed benchmark evaluates the linguistic knowledge of language models on major grammatical phenomena in English. |
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals (2022.acl-long)
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| Challenge: | Current machine learning methods are incapable of efficiently utilizing multimodal information. |
| Approach: | They propose to use text-and-image alignment to improve machine learning's performance on multimodal event sequencing. |
| Outcome: | The proposed models perform significantly worse than humans on multimodal event sequencing than humans. |
Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking (2023.eacl-main)
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Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, Michael Witbrock
| Challenge: | Existing scientific fact-checking datasets are limited due to expertise bottleneck . multi2Claim pipeline is a tool to convert multiple-choice questions into fact- checking data . |
| Approach: | They propose a pipeline for automatically converting multiple-choice questions into fact-checking data . they generate two large-scale datasets for scientific-fact-checker tasks . success at this task can help the reader understand scientific topics and promote science . |
| Outcome: | The proposed pipeline improves performance on two large-scale scientific fact-checking datasets. |
Can Large Language Models Learn Independent Causal Mechanisms? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform poorly on complex reasoning tasks, such as abstract, causal, or logical reasoning. |
| Approach: | They propose to use two concepts from causality to learn ICMs within LLMs to improve out-of-distribution performance on abstract and causal reasoning tasks. |
| Outcome: | The proposed model outperforms existing models on abstract and causal reasoning tasks and is more robust to fine-tuning. |
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. |