A key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects. It thereby casts the burden of ensuring grammaticality, faithfulness, and controllability all on the LMs. We propose Pangu, a generic framework for grounded language understanding that capitalizes on the discriminative ability of LMs instead of their generative ability. Pangu consists of a symbolic agent and a neural LM working in a concerted fashion: The agent explores the environment to incrementally construct valid plans, and the LM evaluates the plausibility of the candidate plans to guide the search process. A case study on the challenging problem of knowledge base question answering (KBQA), which features a massive environment, demonstrates the remarkable effectiveness and flexibility of Pangu: A BERT-base LM is sufficient for setting a new record on standard KBQA datasets, and larger LMs further bring substantial gains. Pangu also enables, for the first time, effective few-shot in-context learning for KBQA with large LMs such as Codex.
Beyond "I Don't Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China（计算机科学与技术系，人工智能研究院，清华大学，北京，中国）；Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China（人工智能产业研究院（AIR），清华大学，北京，中国）
"You are grounded!": Latent Name Artifacts in Pre-trained Language Models
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；University of Maryland, College Park（马里兰大学学院市分校）
预训练的语言模型（LM）可能会将源自其训练语料库的偏见延续到下游模型。我们专注于与给定名称的表示相关的工件（例如，Donald），其取决于语料库，可以与特定实体相关联，如由下一个令牌预测（例如，特朗普）。虽然在某些情况下是有帮助的，但接地也发生在不明确或不适当的情况下。例如，“Donald is a”的结尾与其他名字的结尾有很大不同，并且往往具有高于平均水平的负面情绪。我们展示了潜在的影响下游任务与阅读理解探针名称扰动改变模型的答案。作为一线希望，我们的实验表明，在不同语料库上进行额外的预训练可能会减轻这种偏见。
Hypothesis-Conditioned Query Rewriting for Decision-Useful Retrieval
Graduate School of AI, KAIST, Republic of Korea（韩国科学技术院人工智能研究生院）；School of Electrical Engineering, KAIST, Republic of Korea（韩国科学技术院电气工程学院）；Department of Industrial Engineering, Yonsei University, Republic of Korea（延世大学工业工程系）
Northeastern Uni at Multilingual Counterspeech Generation: Enhancing Counter Speech Generation with LLM Alignment through Direct Preference Optimization
Capital One（第一资本）；Northeastern University（东北大学）；Apple Inc.（苹果公司）
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World
Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry
Department of Robotics and Mechatronics Engineering, University of Dhaka（达卡大学机器人与机电工程系）；Department of Computer Science and Engineering, University of Dhaka（达卡大学计算机科学与工程系）；Department of Agronomy, Gazipur Agricultural University（加兹ipur农业大学作物学系）；Department of Botany, University of Dhaka（达卡大学植物学系）
