Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation

• URL: http://arxiv.org/abs/2310.10706v2
• Date: Wed, 18 Oct 2023 00:55:57 GMT
• Title: Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation
• Authors: Zijian Ding, Alison Smith-Renner, Wenjuan Zhang, Joel R. Tetreault, Alejandro Jaimes
• Abstract summary: This study explores how humans can best leverage LLMs for writing and how interacting with these models affects feelings of ownership and trust in the writing process. While LLMs alone can generate satisfactory news headlines, on average, human control is needed to fix undesirable model outputs.
• Score: 58.31430028519306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To explore how humans can best leverage LLMs for writing and how interacting with these models affects feelings of ownership and trust in the writing process, we compared common human-AI interaction types (e.g., guiding system, selecting from system outputs, post-editing outputs) in the context of LLM-assisted news headline generation. While LLMs alone can generate satisfactory news headlines, on average, human control is needed to fix undesirable model outputs. Of the interaction methods, guiding and selecting model output added the most benefit with the lowest cost (in time and effort). Further, AI assistance did not harm participants' perception of control compared to freeform editing.

Related papers

• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Leveraging LLMs for Dialogue Quality Measurement [27.046917937460798]
  Large language models (LLMs) show robust zeroshot and few-shot capabilities across NLP tasks. Manipulating factors such as model size, in-context examples, and selection techniques, we examine "chain-of-thought" (CoT) reasoning and label extraction procedures. Our results indicate that LLMs that are suitably fine-tuned and have sufficient reasoning capabilities can be leveraged for automated dialogue evaluation.
  arXiv  Detail & Related papers  (2024-06-25T06:19:47Z)
• LLMs Could Autonomously Learn Without External Supervision [36.36147944680502]
  Large Language Models (LLMs) have traditionally been tethered to human-annotated datasets and predefined training objectives. This paper presents a transformative approach: Autonomous Learning for LLMs. This method endows LLMs with the ability to self-educate through direct interaction with text, akin to a human reading and comprehending literature.
  arXiv  Detail & Related papers  (2024-06-02T03:36:37Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation [15.718288693929019]
  Large Language Models (LLM) achieve state-of-the-art performance on many NLP tasks. We study whether LLMs can be used as substitutes for human annotators. We find that LLMs outperform current automatic measures for system-level evaluation but still struggle to provide satisfactory explanations.
  arXiv  Detail & Related papers  (2024-05-22T15:56:52Z)
• GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications [46.85306320942487]
  Large Language Models (LLMs) are evolving to actively engage with tools and performing actions on real-world applications and services. Today, humans verify the correctness and appropriateness of the LLM-generated outputs before putting them into real-world execution. This poses significant challenges as code comprehension is well known to be notoriously difficult. In this paper, we study how humans can efficiently collaborate with, delegate to, and supervise autonomous LLMs in the future.
  arXiv  Detail & Related papers  (2024-04-10T11:17:33Z)
• Machine Unlearning in Large Language Models [8.14992136443131]
  This paper introduces a novel machine unlearning framework into large language models. Our objectives are to make LLMs not produce harmful, hallucinatory, or privacy-compromising responses. Experimental results show that our approach effectively meets unlearning objectives without substantially compromising model performance.
  arXiv  Detail & Related papers  (2024-02-03T05:14:56Z)
• 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)
• Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System [65.93577256431125]
  We propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller task-oriented dialogue model. This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. Our approach outperforms previous state-of-the-art (SOTA) results.
  arXiv  Detail & Related papers  (2023-06-16T13:04:56Z)
• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision [84.31474052176343]
  Recent AI-assistant agents, such as ChatGPT, rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback to align the output with human intentions. This dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision. We propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision.
  arXiv  Detail & Related papers  (2023-05-04T17:59:28Z)

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.