Retcon -- a Prompt-Based Technique for Precise Control of LLMs in Conversations
- URL: http://arxiv.org/abs/2603.03317v1
- Date: Mon, 09 Feb 2026 18:32:28 GMT
- Title: Retcon -- a Prompt-Based Technique for Precise Control of LLMs in Conversations
- Abstract summary: Retcon is a few-shot prompting technique designed to provide turn-level control over LLMs in conversations.<n>We demonstrate that it performs significantly better than zero-shot and traditional few-shot prompting.
- Score: 5.105686385036258
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Recent advances in Large Language Models (LLMs) allow agents to execute complex natural language tasks. Many LLM applications, such as support agents, teaching assistants, and interactive bots, involve multi-turn conversations. However, it remains challenging to control LLMs in the context of such interactions, particularly when the LLM behavior needs to be adjustable over the course of the conversation. In this paper, we present Retcon, a few-shot prompting technique designed to provide turn-level control over LLMs in conversations. We then demonstrate that it performs significantly better than zero-shot and traditional few-shot prompting.
Related papers
- LLMs Get Lost In Multi-Turn Conversation [44.26588510453331]
Large Language Models (LLMs) are conversational interfaces.<n>LLMs have the potential to assist their users not only when they can fully specify the task at hand, but also to help them define, explore, and refine what they need through multi-turn conversational exchange.
arXiv Detail & Related papers (2025-05-09T15:21:44Z) - 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.<n>We use WavLM and Whisper encoder to extract multi-faceted speech representations that are sensitive to speaker characteristics and semantic context.<n>Experiments reveal the promising performance of our proposed system, MT-LLM, in cocktail party scenarios.
arXiv Detail & Related papers (2024-09-13T07:28:28Z) - Intermittent Semi-working Mask: A New Masking Paradigm for LLMs [13.271151693864114]
Multi-turn dialogues are a key interaction method between humans and Large Language Models (LLMs)
We propose a novel masking scheme called Intermittent Semi-working Mask (ISM) to address these problems.
arXiv Detail & Related papers (2024-08-01T13:22:01Z) - RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents [27.807695570974644]
We propose a novel method, textscRePrompt, which does agradient descent"-like approach to optimize the step-by-step instructions in the prompts given to LLM agents.<n>By leveraging intermediate feedback, textscRePrompt can optimize the prompt without the need for a final solution checker.
arXiv Detail & Related papers (2024-06-17T01:23:11Z) - MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions [58.57255822646756]
This paper introduces MathChat, a benchmark designed to evaluate large language models (LLMs) across a broader spectrum of mathematical tasks.
We evaluate the performance of various SOTA LLMs on the MathChat benchmark, and we observe that while these models excel in single turn question answering, they significantly underperform in more complex scenarios.
We develop MathChat sync, a synthetic dialogue based math dataset for LLM finetuning, focusing on improving models' interaction and instruction following capabilities in conversations.
arXiv Detail & Related papers (2024-05-29T18:45:55Z) - Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the
Key? [84.36332588191623]
We propose a novel group discussion framework to enrich the set of discussion mechanisms.
We observe that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt.
arXiv Detail & Related papers (2024-02-28T12:04:05Z) - LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning [67.39585115936329]
We argue that LLMs have inherent capabilities to handle long contexts without fine-tuning.
We propose SelfExtend to extend the context window of LLMs by constructing bi-level attention information.
We conduct comprehensive experiments on multiple benchmarks and the results show that our SelfExtend can effectively extend existing LLMs' context window length.
arXiv Detail & Related papers (2024-01-02T18:30:51Z) - LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language
Models [56.25156596019168]
This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs)
Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
arXiv Detail & Related papers (2023-11-30T03:59:31Z) - Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations [70.7884839812069]
Large language models (LLMs) have emerged as powerful and general solutions to many natural language tasks.
However, many of the most important applications of language generation are interactive, where an agent has to talk to a person to reach a desired outcome.
In this work, we explore a new method for adapting LLMs with RL for such goal-directed dialogue.
arXiv Detail & Related papers (2023-11-09T18:45:16Z) - Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach [31.6589518077397]
Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets.
LLMs can assist an embodied agent in solving complex sequential decision making tasks by providing high-level instructions.
We propose When2Ask, a reinforcement learning based approach that learns when it is necessary to query LLMs for high-level instructions.
arXiv Detail & Related papers (2023-06-06T11:49:09Z) - Low-code LLM: Graphical User Interface over Large Language Models [115.08718239772107]
This paper introduces a novel human-LLM interaction framework, Low-code LLM.
It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses.
We highlight three advantages of the low-code LLM: user-friendly interaction, controllable generation, and wide applicability.
arXiv Detail & Related papers (2023-04-17T09:27:40Z)
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.