LLMs syntactically adapt their language use to their conversational partner
- URL: http://arxiv.org/abs/2503.07457v1
- Date: Mon, 10 Mar 2025 15:37:07 GMT
- Title: LLMs syntactically adapt their language use to their conversational partner
- Authors: Florian Kandra, Vera Demberg, Alexander Koller,
- Abstract summary: It has been frequently observed that human speakers align their language use with each other during conversations.<n>We construct a corpus of conversations between large language models (LLMs) and find that two LLM agents end up making more similar syntactic choices as conversations go on.
- Score: 58.92470092706263
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: It has been frequently observed that human speakers align their language use with each other during conversations. In this paper, we study empirically whether large language models (LLMs) exhibit the same behavior of conversational adaptation. We construct a corpus of conversations between LLMs and find that two LLM agents end up making more similar syntactic choices as conversations go on, confirming that modern LLMs adapt their language use to their conversational partners in at least a rudimentary way.
Related papers
- Shaping Shared Languages: Human and Large Language Models' Inductive Biases in Emergent Communication [0.09999629695552195]
We investigate how artificial languages evolve when optimised for inductive biases in humans and large language models (LLMs)<n>We show that referentially grounded vocabularies emerge that enable reliable communication in all conditions, even when humans and LLMs collaborate.
arXiv Detail & Related papers (2025-03-06T12:47:54Z) - SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken Dialogue Generation [56.683846056788326]
We propose SLM and LLM Integration for spontaneous spoken Dialogue gEneration.<n>We convert the textual dialogues into phoneme sequences and use a two-tower transformer-based duration predictor to predict the duration of each phoneme.<n> Experimental results on the Fisher dataset demonstrate that our system can generate naturalistic spoken dialogue while maintaining high semantic coherence.
arXiv Detail & Related papers (2025-01-01T11:11:07Z) - Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
Large Language Models (LLMs) have shown impressive language capabilities.
In this work, we investigate the spontaneous multilingual alignment improvement of LLMs.
We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
arXiv Detail & Related papers (2024-05-22T16:46:19Z) - Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations [65.29513437838457]
Even if two current turns are the same sentence, their responses might still differ when they are spoken in different styles.
We propose Spoken-LLM framework that can model the linguistic content and the speaking styles.
We train Spoken-LLM using the StyleTalk dataset and devise a two-stage training pipeline to help the Spoken-LLM better learn the speaking styles.
arXiv Detail & Related papers (2024-02-20T07:51:43Z) - Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study [3.0059120458540383]
We consider the evaluation of the lexical richness of the text generated by conversational Large Language Models (LLMs) and how it depends on the model parameters.
The results show how lexical richness depends on the version of ChatGPT and some of its parameters, such as the presence penalty, or on the role assigned to the model.
arXiv Detail & Related papers (2024-02-11T13:41:17Z) - LLM Agents in Interaction: Measuring Personality Consistency and
Linguistic Alignment in Interacting Populations of Large Language Models [4.706971067968811]
We create a two-group population of large language models (LLMs) agents using a simple variability-inducing sampling algorithm.
We administer personality tests and submit the agents to a collaborative writing task, finding that different profiles exhibit different degrees of personality consistency and linguistic alignment to their conversational partners.
arXiv Detail & Related papers (2024-02-05T11:05:20Z) - Boosting Large Language Model for Speech Synthesis: An Empirical Study [86.89548753080432]
Large language models (LLMs) have made significant advancements in natural language processing and are concurrently extending the language ability to other modalities, such as speech and vision.
We conduct a comprehensive empirical exploration of boosting LLMs with the ability to generate speech, by combining pre-trained LLM LLaMA/OPT and text-to-speech synthesis model VALL-E.
We compare three integration methods between LLMs and speech models, including directly fine-tuned LLMs, superposed layers of LLMs and VALL-E, and coupled LLMs and VALL-E using LLMs as a powerful text encoder
arXiv Detail & Related papers (2023-12-30T14:20:04Z) - Probing LLMs for Joint Encoding of Linguistic Categories [10.988109020181563]
We propose a framework for testing the joint encoding of linguistic categories in Large Language Models (LLMs)
We find evidence of joint encoding both at the same (related part-of-speech (POS) classes) and different (POS classes and related syntactic dependency relations) levels of linguistic hierarchy.
arXiv Detail & Related papers (2023-10-28T12:46:40Z) - Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue.
By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights.
This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
arXiv Detail & Related papers (2023-10-10T03:06:38Z)
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.