From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models

• URL: http://arxiv.org/abs/2401.02777v2
• Date: Tue, 30 Jan 2024 07:02:30 GMT
• Title: From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
• Authors: Na Liu, Liangyu Chen, Xiaoyu Tian, Wei Zou, Kaijiang Chen, Ming Cui
• Abstract summary: RAISE (Reasoning and Acting through Scratchpad and Examples) is an advanced architecture enhancing the integration of Large Language Models (LLMs) into conversational agents. It incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in conversations.
• Score: 11.999652715036643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large Language Models (LLMs) like GPT-4 into conversational agents. RAISE, an enhancement of the ReAct framework, incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in conversations. It entails a comprehensive agent construction scenario, including phases like Conversation Selection, Scene Extraction, CoT Completion, and Scene Augmentation, leading to the LLMs Training phase. This approach appears to enhance agent controllability and adaptability in complex, multi-turn dialogues. Our preliminary evaluations in a real estate sales context suggest that RAISE has some advantages over traditional agents, indicating its potential for broader applications. This work contributes to the AI field by providing a robust framework for developing more context-aware and versatile conversational agents.

Related papers

• Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions [68.98811048970963]
  We present a pioneering effort to investigate the capability of large language models (LLMs) in transcribing speech in multi-talker environments. Our approach utilizes WavLM and Whisper encoder to extract multi-faceted speech representations that are sensitive to speaker characteristics and semantic context. Comprehensive experiments reveal the promising performance of our proposed system, MT-LLM, in cocktail party scenarios.
  arXiv  Detail & Related papers  (2024-09-13T07:28:28Z)
• AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction [10.65417796726349]
  relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence. We propose an agent-based RE framework, namely AgentRE, which fully leverages the potential of large language models to achieve RE in complex scenarios.
  arXiv  Detail & Related papers  (2024-09-03T12:53:05Z)
• The Compressor-Retriever Architecture for Language Model OS [20.56093501980724]
  This paper explores the concept of using a language model as the core component of an operating system (OS) A key challenge in realizing such an LM OS is managing the life-long context and ensuring statefulness across sessions. We introduce compressor-retriever, a model-agnostic architecture designed for life-long context management.
  arXiv  Detail & Related papers  (2024-09-02T23:28:15Z)
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue [63.65128176360345]
  We introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent) It incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation. The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated.
  arXiv  Detail & Related papers  (2024-06-09T21:58:32Z)
• HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models [13.963676467274109]
  We extend the capabilities of HELPER by expanding its memory with a wider array of examples and prompts. This simple expansion of HELPER into a shared memory enables the agent to work across domains executing plans from dialogue, natural language instruction, active question asking, and common room reorganization. We evaluate the agent on four diverse interactive visual-language embodied agent: AChRED, TEA, DialFRED, and the Tidy Task.
  arXiv  Detail & Related papers  (2024-04-29T19:12:42Z)
• DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton [44.26173742405563]
  This paper introduces the retrieval-augmented large language model with Definite Finite Automaton (DFA-RAG) DFA-RAG is a framework designed to enhance the capabilities of conversational agents using large language models (LLMs)
  arXiv  Detail & Related papers  (2024-02-06T21:14:45Z)
• DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever [83.33209603041013]
  We propose a parameter-efficient prompt-tuning method named DialCLIP for multi-modal dialog retrieval. Our approach introduces a multi-modal context generator to learn context features which are distilled into prompts within the pre-trained vision-language model CLIP. To facilitate various types of retrieval, we also design multiple experts to learn mappings from CLIP outputs to multi-modal representation space.
  arXiv  Detail & Related papers  (2024-01-02T07:40:12Z)
• Towards More Unified In-context Visual Understanding [74.55332581979292]
  We present a new ICL framework for visual understanding with multi-modal output enabled. First, we quantize and embed both text and visual prompt into a unified representational space. Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them.
  arXiv  Detail & Related papers  (2023-12-05T06:02:21Z)
• InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models [9.611864685207056]
  We propose a novel approach, InstructERC, to reformulate the emotion recognition task from a discriminative framework to a generative framework based on Large Language Models (LLMs) InstructERC makes three significant contributions: (1) it introduces a simple yet effective retrieval template module, which helps the model explicitly integrate multi-granularity dialogue supervision information; (2) we introduce two additional emotion alignment tasks, namely speaker identification and emotion prediction tasks, to implicitly model the dialogue role relationships and future emotional tendencies in conversations; and (3) Pioneeringly, we unify emotion labels across benchmarks through the feeling wheel to fit real application scenarios.
  arXiv  Detail & Related papers  (2023-09-21T09:22:07Z)
• A Mixture-of-Expert Approach to RL-based Dialogue Management [56.08449336469477]
  We use reinforcement learning to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with aly complex action space even for a medium-size vocabulary. We develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of specialized LMs (or experts) capable of generating utterances corresponding to a
  arXiv  Detail & Related papers  (2022-05-31T19:00:41Z)
• Video-Grounded Dialogues with Pretrained Generation Language Models [88.15419265622748]
  We leverage the power of pre-trained language models for improving video-grounded dialogue. We propose a framework by formulating sequence-to-grounded dialogue tasks as a sequence-to-grounded task. Our framework allows fine-tuning language models to capture dependencies across multiple modalities.
  arXiv  Detail & Related papers  (2020-06-27T08:24:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.