10 accepted papers
Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a significant challenge for even state-of-the-art Large Language Models (LLMs). A lack of task-specific data often limits previou
Large Language Models (LLMs) have shown remarkable reasoning capabilities, while their practical applications are limited by severe factual hallucinations due to limitations in the timeliness, accuracy, and comprehensiveness of their parametric knowledge. Meanwhile, enhancing retrieval-augmented gen…
Open-Domain Question Answering (ODQA) systems often struggle with the quality of retrieved passages, which may contain conflicting information and be misaligned with the reader’s needs. Existing retrieval methods aim to gather relevant passages but often fail to prioritize consistent and useful info…
Understanding the mechanisms underlying Large Language Model (LLM) behavior in Retrieval-Augmented Generation (RAG) systems is critical for enhancing reliability. In this paper, we leverage Sparse Autoencoders (SAEs) within the LLaMA Scope to uncover sparse, interpretable latents that govern RAG beh…
Low-Rank Adaptation (LoRA) is a widespread parameter-efficient fine-tuning algorithm for large-scale language models. It has been commonly accepted that LoRA mostly achieves promising results in single-task, low-resource settings, and struggles to handle multi-task instruction tuning scenarios. In t…
Despite the advancements made with the retrieve-then-read pipeline on open-domain question answering task, current methods still face challenges stemming from term mismatch and limited interaction between information retrieval systems and large language models. To mitigate these issues, we propose t…
Current neural semantic parsers take a supervised approach requiring a considerable amount of training data which is expensive and difficult to obtain. Thus, minimizing the supervision effort is one of the key challenges in semantic parsing. In this paper, we propose the Retrieval as Ambiguous Super…
Dialogue rewriting aims to transform multi-turn, context-dependent dialogues into well-formed, context-independent text for most NLP systems. Previous dialogue rewriting benchmarks and systems assume a fluent and informative utterance to rewrite. Unfortunately, dialogue utterances from real-world sy…
Since the meaning representations are detailed and accurate annotations which express fine-grained sequence-level semtantics, it is usually hard to train discriminative semantic parsers via Maximum Likelihood Estimation (MLE) in an autoregressive fashion. In this paper, we propose a semantic-aware c…
Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by join…