Papers by Margaret Li
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds (2021.naacl-main)
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| Challenge: | a recent improvement in the quality of natural language processing and generation (NLG) is needed for goal-oriented ML driven agents. |
| Approach: | They propose a reinforcement learning system that integrates large-scale language modeling and commonsense reasoning-based pre-training to imbue the agent with relevant priors. |
| Outcome: | The proposed system is able to act and talk naturally with respect to their motivations. |
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (P19-1)
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| Challenge: | EmpatheticDialogues dataset provides a benchmark for empathetic dialogue generation . human evaluators perceive dialogue models as more epathetic . |
| Approach: | They propose a benchmark for empathetic dialogue generation from a dataset of 25k conversations grounded in emotional situations. |
| Outcome: | The proposed benchmarks show that existing models are perceived to be more empathetic by human evaluators compared to models trained on large-scale Internet conversations. |
Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training (2020.acl-main)
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| Challenge: | Unlikelihood is a technique developed for removal of repetition in language model completions . it allows for a model to be generalized to solve a number of problems . |
| Approach: | They extend the unlikelihood objective to generate generations that contain repetitions . they show that such an objective can be used to improve logical consistency . |
| Outcome: | The proposed approach can be applied to a number of dialogue tasks. |
Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models (2024.emnlp-main)
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Terra Blevins, Tomasz Limisiewicz, Suchin Gururangan, Margaret Li, Hila Gonen, Noah Smith, Luke Zettlemoyer
| Challenge: | Multilingual language models often underperform monolingual ones due to inter-language competition for model parameters. |
| Approach: | They propose Cross-lingual Expert Language Models (X-ELM) which mitigates inter-language competition by independently training language models on subsets of the multilingual corpus. |
| Outcome: | The proposed model outperforms jointly trained multilingual models across all 16 considered languages and transfer the gains to downstream tasks. |
Byte Latent Transformer: Patches Scale Better Than Tokens (2025.acl-long)
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Artidoro Pagnoni, Ramakanth Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason E Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srini Iyer
| Challenge: | Existing large language models (LLMs) are trained on bytes, except for tokenization, which groups bytes into a static set of tokens. |
| Approach: | They propose a new byte-level LLM architecture that encodes bytes into dynamically sized patches, which serve as the primary units of computation. |
| Outcome: | The proposed architecture matches tokenization-based models with improvements in inference efficiency and robustness. |
Bot-Adversarial Dialogue for Safe Conversational Agents (2021.naacl-main)
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| Challenge: | a new method for evaluating chatbot safety is proposed to mimic human-generated data . a bot-adversarial dialogue model learns undesirable features from this data, a study finds . |
| Approach: | They propose a human-and-model-in-the-loop framework for evaluating toxicity of chatbots . they propose two methods for safe conversational agents by either training on data or ”baking-in” safety to the generative model itself. |
| Outcome: | The proposed methods are safer than existing models while maintaining usability metrics, the authors say . they show that the proposed methods can be used to make safer models with human-model interactions . |