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ADaPT: As-Needed Decomposition and Planning with Language Models
Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, Tushar KhotNAACL Findings • 2024 Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. Recent works employ LLMs-as-agents in broadly two ways: iteratively determining the next action (iterative…Evaluating In-Context Learning of Libraries for Code Generation
Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep DasigiNAACL • 2024 Contemporary Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. A particularly promising area is their ability to interpret code modules from unfamiliar libraries for solving user-instructed tasks. Recent work…Leveraging Code to Improve In-context Learning for Semantic Parsing
Ben Bogin, Shivanshu Gupta, Peter Clark, Ashish SabharwalNAACL • 2024 In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot nature and improved generalization. However, learning to parse to rare domain-specific languages (DSLs) from just a few demonstrations is challenging, limiting the…QualEval: Qualitative Evaluation for Model Improvement
Vishvak Murahari, Ameet Deshpande, Peter Clark, Tanmay Rajpurohit, Ashish Sabharwal, Karthik Narasimhan, Ashwin KalyanNAACL • 2024 Quantitative evaluation metrics have traditionally been pivotal in gauging the advancements of artificial intelligence systems, including large language models (LLMs). However, these metrics have inherent limitations. Given the intricate nature of real-world…The Bias Amplification Paradox in Text-to-Image Generation
P. Seshadri, Sameer Singh, Yanai ElazarNAACL • 2024 Bias amplification is a phenomenon in which models increase imbalances present in the training data. In this paper, we study bias amplification in the text-to-image domain using Stable Diffusion by comparing gender ratios in training vs. generated images. We…Mitigating Barriers to Public Social Interaction with Meronymous Communication
Nouran Soliman, Hyeonsu B. Kang, Matthew Latzke, Jonathan Bragg, Joseph Chee Chang, Amy X. Zhang, David R. KargerCHI • 2024 In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and hide the social context that motivates people to participate and helps…PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers
Yoonjoo Lee, Hyeonsu B Kang, Matt Latzke, Juho Kim, Jonathan Bragg, Joseph Chee Chang, Pao SiangliulueCHI • 2024 With the rapid growth of scholarly archives, researchers subscribe to"paper alert"systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle…Improving Language Models with Advantage-based Offline Policy Gradients
Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, Mark O. RiedlICLR • 2024 Language Models (LMs) achieve substantial language capabilities when finetuned using Reinforcement Learning with Human Feedback (RLHF). However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for…Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Shashank Gupta, Vaishnavi Shrivastava, A. Deshpande, A. Kalyan, Peter Clark, Ashish Sabharwal, Tushar KhotICLR • 2024 Recent works have showcased the ability of LLMs to embody diverse personas in their responses, exemplified by prompts like 'You are Yoda. Explain the Theory of Relativity.' While this ability allows personalization of LLMs and enables human behavior…BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models
Qingqing Cao, Sewon Min, Yizhong Wang, Hannaneh HajishirziICLR • 2024 Retrieval augmentation addresses many critical problems in large language models such as hallucination, staleness, and privacy leaks. However, running retrieval-augmented language models (LMs) is slow and difficult to scale due to processing large amounts of…