Long-horizon conversational agents need persistent memory beyond a fixed context window, yet memory effectiveness depends on the units stored and retrieved. Existing systems either discard fine-grained details during compression, retrieve units that are too coarse or context-poor, or fragment discourse into entity-relation triples. We propose Neo-Davidsonian Discourse Units: enriched Elementary Discourse Units (EDUs) that each express one event or fact with normalized entities, temporal grounding, and enough context to stand alone. The resulting units are non-lossy at the session level, atomic for retrieval, and naturally expose event arguments for graph-based associative recall. We instantiate this representation in EMem, which retrieves EDUs with dense representations and relevance filtering based on large language models, and EMem-G, which further organizes sessions, EDUs, and arguments in an event graph and applies Personalized PageRank. On LoCoMo and LongMemEval_S benchmarks, our methods improve over prior state-of-the-art by up to 21.3% while using up to 7× shorter effective context.

The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist agents capable of operating within complex environments. These environments are often highly expansive, making it impossible for the LLM to process them within its short-term memory. Motivated by recent research on extending the capabilities of LLMs with tools, we seek to investigate the intriguing potential of tools to augment LLMs in handling such complexity by introducing a novel class of tools, termed middleware, to aid in the proactive exploration within these massive environments. Such specialized tools can serve as a middleware layer shielding the LLM from environmental complexity. In two representative complex environments—knowledge bases (KBs) and databases—we demonstrate the significant potential of augmenting language agents with tools in complex environments. Notably, equipped with the middleware, GPT-4 achieves 2.8X the performance of the best baseline in tasks requiring access to database content and 2.2X in KB tasks. Our findings illuminate the path for advancing language agents in real-world applications.

Grounding language models (LMs) to knowledge bases (KBs) helps to obtain rich and accurate facts. However, it remains challenging because of the enormous size, complex structure, and partial observability of KBs. One reason is that current benchmarks fail to reflect robustness challenges and fairly evaluate models.This paper analyzes whether these robustness challenges arise from distribution shifts, including environmental, linguistic, and modal aspects.This affects the ability of LMs to cope with unseen schema, adapt to language variations, and perform few-shot learning. Thus, the paper proposes extensive evaluation protocols and conducts experiments to demonstrate that, despite utilizing our proposed data augmentation method, both advanced small and large language models exhibit poor robustness in these aspects. We conclude that current LMs are too fragile to navigate in complex environments due to distribution shifts. This underscores the need for future research focusing on data collection, evaluation protocols, and learning paradigms.

Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios. However, KBQA remains challenging, especially regarding coverage and generalization settings. This is due to two main factors: i) understanding the semantics of both questions and relevant knowledge from the KB; ii) generating executable logical forms with both semantic and syntactic correctness. In this paper, we present a new KBQA model, TIARA, which addresses those issues by applying multi-grained retrieval to help the PLM focus on the most relevant KB context, viz., entities, exemplary logical forms, and schema items. Moreover, constrained decoding is used to control the output space and reduce generation errors. Experiments over important benchmarks demonstrate the effectiveness of our approach. TIARA outperforms previous SOTA, including those using PLMs or oracle entity annotations, by at least 4.1 and 1.1 F1 points on GrailQA and WebQuestionsSP, respectively. Specifically on GrailQA, TIARA outperforms previous models in all categories, with an improvement of 4.7 F1 points in zero-shot generalization.

Question answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing. Recently, generation-based methods that translate natural language questions to executable logical forms have achieved promising performance. These methods use auxiliary information to augment the logical form generation of questions with unseen KB items or novel combinations, but the noise introduced can also leads to more incorrect results. In this work, we propose GMT-KBQA, a Generation-based KBQA method via Multi-Task learning, to better retrieve and utilize auxiliary information. GMT-KBQA first obtains candidate entities and relations through dense retrieval, and then introduces a multi-task model which jointly learns entity disambiguation, relation classification, and logical form generation. Experimental results show that GMT-KBQA achieves state-of-the-art results on both ComplexWebQuestions and WebQuestionsSP datasets. Furthermore, the detailed evaluation demonstrates that GMT-KBQA benefits from the auxiliary tasks and has a strong generalization capability.