Papers by Kyle Richardson
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment (2022.emnlp-main)
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| Challenge: | Recent studies have focused on instruction learning, where a model learns to perform unseen tasks from task descriptions alone. |
| Approach: | They propose to use a controlled synthetic environment to characterize large transformer models as instruction learners. |
| Outcome: | The proposed model can interpret only 65.6% of test instructions and 11%-24% of instructions in out-of-distribution settings. |
SELFGOAL: Your Language Agents Already Know How to Achieve High-level Goals (2025.naacl-long)
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Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang
| Challenge: | Existing approaches to improve the performance of language agents without training are not available. |
| Approach: | They propose an automatic approach to break down high-level goals into tree structure of more practical subgoals during interaction with environments while identifying the most useful subgoal. |
| Outcome: | The proposed approach significantly improves the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments. |
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models (2022.starsem-1)
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| Challenge: | ArgumentAnalyst is a multi-dimensional, modular framework for performing deep argument analysis using existing pre-trained language models (PTLMs). |
| Approach: | They propose a multi-dimensional, modular framework for performing deep argument analysis using current pre-trained language models (PTLMs) ArgumentAnalyst reconstructs argumentative texts as valid arguments by inserting missing premises and conclusions, formalizing inferences, and coherently linking the reconstruction to the source text. |
| Outcome: | The proposed framework can replicate established heuristics, cope with plurality of correct solutions, and exploit higher-order evidence. |
A Dataset for Tracking Entities in Open Domain Procedural Text (2020.emnlp-main)
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Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy
| Challenge: | Existing tasks require only a small set of attributes to track state changes in procedural text. |
| Approach: | They propose a task where given a procedural text as input, the task is to generate a set of state change tuples for each step. |
| Outcome: | The proposed task generates state change tuples from a set of pre-defined attributes for each step and predicts them from an open vocabulary. |
DISCO: Distilling Counterfactuals with Large Language Models (2023.acl-long)
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| Challenge: | high-quality counterfactual data is scarce for most tasks and not easily generated at scale. |
| Approach: | They propose a method for automatically generating high-quality counterfactual data at scale . they use a large general language model to generate phrasal perturbations and filter them . |
| Outcome: | The proposed method is task-agnostic and can be applied to the task of natural language inference. |
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents (2025.emnlp-demos)
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Haofei Yu, Keyang Xuan, Fenghai Li, Kunlun Zhu, Zijie Lei, Jiaxun Zhang, Ziheng Qi, Kyle Richardson, Jiaxuan You
| Challenge: | Existing research systems often design and use agentic workflows to perform research tasks such as ideation, scientific coding, review writing, and tree-based search. |
| Approach: | They propose an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer. |
| Outcome: | The proposed framework adapts easily to new tools and supports iterative growth. |
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)
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| Challenge: | Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings. |
| Approach: | They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset. |
| Outcome: | The proposed model trains on Chinese and English natural language inference datasets. |
Temporal Reasoning on Implicit Events from Distant Supervision (2021.naacl-main)
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| Challenge: | a novel temporal reasoning dataset evaluates the degree to which systems understand implicit events . state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events - a recent paper . |
| Approach: | They propose a temporal reasoning dataset that evaluates the degree to which systems understand implicit events. |
| Outcome: | The proposed model outperforms baseline systems on TRACIE by 5% and 11% on MATRES, an explicit event benchmark. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Language Models with Rationality (2023.emnlp-main)
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| Challenge: | lack of interpretability is a growing impediment to widespread use of large language models . a new approach to solve this problem is to add a rational layer on top of the LLM . |
| Approach: | They propose to add a rational layer to the large language models to make model beliefs explicit . they also propose to identify and minimize contradictions in the model belief graph . |
| Outcome: | a new approach improves consistency without harming overall answer accuracy . the proposed approach makes model beliefs explicit and resolves inconsistencies . |
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts (2022.naacl-main)
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Daniel Khashabi, Xinxi Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, Yejin Choi
| Challenge: | Recent work shows the surprising power of continuous prompts to language models for controlled generation and solving a wide range of tasks. |
| Approach: | They propose to extract a discrete (textual) interpretation of continuous prompts faithful to the problem they solve. |
| Outcome: | The proposed model can find prompts that solve a task while being projected to an arbitrary text with a smaller drop in accuracy. |
Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking (2022.starsem-1)
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Ronen Tamari, Kyle Richardson, Noam Kahlon, Aviad Sar-shalom, Nelson F. Liu, Reut Tsarfaty, Dafna Shahaf
| Challenge: | Controlled synthetic tasks are an important resource for diagnosing model behavior. |
| Approach: | They propose a framework that provides fine-grained control over task generation in bAbI. |
| Outcome: | The proposed framework provides fine-grained control over task generation in the bAbI benchmark. |
OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)
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| Challenge: | Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets. |
| Approach: | They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations. |
| Outcome: | The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics. |
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs (2022.emnlp-main)
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| Challenge: | a framework for language understanding models to track and improve beliefs through intermediate points in text is needed . breakpoint modeling is an efficient and end-to-end learning approach that trains models to train beliefs . understanding the behavior of models remains a formidable challenge for model safety, authors say . |
| Approach: | They propose a framework that trains models to track beliefs through intermediate points in text . their framework allows for efficient and robust learning of this type of model . |
| Outcome: | The proposed model outperforms strong representation learning approaches on a variety of NLU tasks. |
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts (2022.emnlp-main)
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| Challenge: | Existing approaches to end-to-end questionanswering assume that pre-trained language can decompose complex tasks into more straightforward sub-tasks. |
| Approach: | They propose to use distant supervision to train decomposition-based transformers for large-scale parallel news. |
| Outcome: | The proposed model improves on semantic parsing and on hotpotQA and strategyQA datasets by 20% to 30%. |
SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories (2024.emnlp-main)
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Ben Bogin, Kejuan Yang, Shashank Gupta, Kyle Richardson, Erin Bransom, Peter Clark, Ashish Sabharwal, Tushar Khot
| Challenge: | Large Language Models (LLMs) have made significant progress in writing code, but can they be used to reproduce results from research repositories? |
| Approach: | They propose a benchmark to evaluate the capability of Large Language Models to reproduce results from research repositories. |
| Outcome: | The benchmark aims to capture the realistic challenges faced by researchers working with machine learning and natural language processing repositories. |
Polyglot Semantic Parsing in APIs (N18-1)
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| Challenge: | Existing approaches to semantic parsing work by training individual models for each available parallel dataset of text-meaning pairs. |
| Approach: | They propose a polyglot semantic translation approach that trains on multiple datasets and natural languages to learn parsing models. |
| Outcome: | The proposed model can be used for parsing a wide variety of natural languages and output languages, and achieves state-of-the-art performance on the above datasets. |
TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation (2024.acl-long)
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| Challenge: | e.g., GPT-4 still lag behind humans in effective multitasking, a study finds . current textual simulations do not adequately address the notion of time . |
| Approach: | They propose a textual simulated environment that incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios. |
| Outcome: | The proposed model incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios. |
Event Causality Identification with Synthetic Control (2024.emnlp-main)
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| Challenge: | Existing approaches to event causality identification have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality . |
| Approach: | They propose to use the Rubin Causal Model to identify event causality by generating a twin from existing corpora. |
| Outcome: | The proposed method can identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard. |
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge (2020.tacl-1)
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| Challenge: | Existing models are far from perfect when assessed at the level of clusters of semantically connected probes, such as all hypernym questions about a single concept. |
| Approach: | They propose a method for automatically building probe datasets from expert knowledge sources, allowing systematic control and a comprehensive evaluation. |
| Outcome: | The proposed model is predisposed to recognize certain types of structural linguistic knowledge, but performance degrades even with a slight increase in the number of “hops” in the underlying taxonomic hierarchy. |
Hey AI, Can You Solve Complex Tasks by Talking to Agents? (2022.findings-acl)
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| Challenge: | CommaQA is a benchmark for learning to solve complex tasks by communicating with existing agents in natural language. |
| Approach: | They propose a synthetic benchmark with three complex reasoning tasks designed to be solved by communicating with existing QA agents. |
| Outcome: | The proposed model outperforms models that learn to communicate with agents without auxiliary supervision or data. |
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models (2021.naacl-main)
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| Challenge: | Existing approaches to decompose complex tasks into simpler ones do not require annotated decompositions. |
| Approach: | They propose a framework for building interpretable systems that learn to solve complex tasks by decomposing existing models into simpler ones solvable by existing models. |
| Outcome: | The proposed framework is more versatile than existing explainable systems for DROP and HotpotQA datasets, is more robust than state-of-the-art blackbox (uninterpretable) systems, and generates more understandable and trustworthy explanations compared to prior work. |