Percy Liang - New Related Research

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Percy Liang - New Related Research

Percy Liang is a renowned researcher in the field of natural language processing (NLP) and artificial intelligence (AI). His work has been instrumental in advancing the capabilities of language models, which are a crucial component of modern AI systems. In this article, we will explore some of the latest research related to Percy Liang, focusing on the development of more advanced and reliable language models.

Answer, Refuse, or Guess? Investigating Risk-Aware Decision Making in Language Models

CK Wu, ZR Tam, CY Lin, YN Chen, H Lee - arXiv preprint arXiv:2503.01332, 2025

Knowing when to answer or refuse is crucial for safe and reliable decision-making language agents. Although prior work has introduced refusal strategies to boost LMs' reliability, how these models adapt their decisions to different risk levels remains unclear. This paper proposes a novel approach to investigate risk-aware decision making in language models, which involves training models to answer, refuse, or guess based on the risk level of the input.

The authors propose a risk-aware decision-making framework that incorporates a risk assessment module and a decision-making module. The risk assessment module evaluates the risk level of the input, while the decision-making module selects the appropriate response based on the risk level. The authors demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of risk-aware decision making.

Multidimensional Consistency Improves Reasoning in Language Models

H Lai, X Zhang, M Nissim - arXiv preprint arXiv:2503.02670, 2025

While Large language models (LLMs) have proved able to address some complex reasoning tasks, we also know that they are highly sensitive to input variation, which can lead to different solution paths and final answers. Answer consistency across different input variations is crucial for reliable reasoning. This paper proposes a novel approach to improve answer consistency in LLMs by incorporating multidimensional consistency.

The authors propose a multidimensional consistency framework that involves training models to generate consistent answers across different input variations. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of answer consistency.

Adding Alignment Control to Language Models

W Zhu, W Zhang, R Wang - arXiv preprint arXiv:2503.04346, 2025

Post-training alignment has increasingly become a crucial factor in enhancing the usability of language models (LMs). However, the strength of alignment varies depending on individual preferences. This paper proposes a method to incorporate alignment control into LMs, which involves training models to adapt to different alignment preferences.

The authors propose an alignment control framework that involves training models to generate aligned responses based on the alignment preference of the user. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of alignment control.

Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models

Z Xie, M Lin, Z Liu, P Wu, S Yan, C Miao - arXiv preprint arXiv:2503.02318, 2025

Recent advancements in multimodal reasoning have largely overlooked the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio tasks. We meticulously curated a large-scale and diverse dataset for audio reasoning, which involves training models to reason about audio inputs.

The authors propose an audio reasoning framework that involves training models to generate aligned responses based on the audio input. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of audio reasoning.

GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation

J He, J Neville, M Wan, L Yang, H Liu, X Xu, X Song… - arXiv preprint arXiv …, 2025

Large Language Models (LLMs) can enhance their capabilities as AI assistants by integrating external tools, allowing them to access a wider range of information. While recent LLMs are typically fine-tuned with tool usage examples during training, this approach has limitations in terms of tool generalization. This paper proposes a novel approach to enhance tool generalization in LLMs through zero-to-one and weak-to-strong simulation.

The authors propose a tool generalization framework that involves training models to simulate tool usage in a zero-to-one and weak-to-strong manner. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of tool generalization.

Can Language Models Falsify? Evaluating Algorithmic Reasoning with Counterexample Creation

S Sinha, S Goel, P Kumaraguru, J Geiping, M Bethge… - arXiv preprint arXiv …, 2025

There is growing excitement about the potential of Language Models (LMs) to accelerate scientific discovery. Falsifying hypotheses is key to scientific progress, as it allows claims to be iteratively refined over time. This process requires significant algorithmic reasoning capabilities, which are still a subject of ongoing research. This paper proposes a novel approach to evaluate algorithmic reasoning in LLMs using counterexample creation.

The authors propose an algorithmic reasoning framework that involves training models to generate counterexamples to hypotheses. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of algorithmic reasoning.

Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment

W Yang, J Wu, C Wang, C Zong, J Zhang - arXiv preprint arXiv:2503.04647, 2025

Direct Preference Optimization (DPO) has become a prominent method for aligning Large Language Models (LLMs) with human preferences. While DPO has enabled significant progress in aligning English LLMs, multilingual preference alignment is still a challenging task. This paper proposes a novel approach to implicit cross-lingual rewarding for efficient multilingual preference alignment.

The authors propose an implicit cross-lingual rewarding framework that involves training models to generate aligned responses based on the preference of the user in multiple languages. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of multilingual preference alignment.

DiffPO: Diffusion-styled Preference Optimization for Efficient Inference-Time Alignment of Large Language Models

R Chen, W Chai, Z Yang, X Zhang, JT Zhou, T Quek… - arXiv preprint arXiv …, 2025

Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. This paper proposes a novel approach to diffusion-styled preference optimization for efficient inference-time alignment of LLMs.

The authors propose a diffusion-styled preference optimization framework that involves training models to generate aligned responses based on the preference of the user in real-time. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of inference-time alignment.

CROWDSELECT: Synthetic Instruction Data Selection with Multi-LLM Wisdom

Y Li, L Yang, W Shen, P Zhou, Y Wan, W Lin, D Chen - arXiv preprint arXiv …, 2025

Distilling advanced Large Language Models' instruction-following capabilities into smaller models using a selected subset has become a mainstream approach in model training. While existing synthetic instruction data selection strategies rely on a single LLM, this approach has limitations in terms of model generalization. This paper proposes a novel approach to synthetic instruction data selection with multi-LLM wisdom.

The authors propose a synthetic instruction data selection framework that involves training models to select a subset of instructions based on the wisdom of multiple LLMs. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of model generalization.

Nature-Inspired Population-Based Evolution of Large Language Models

Y Zhang, P Ye, X Yang, S Feng, S Zhang, L Bai… - arXiv preprint arXiv …, 2025

Evolution, the engine behind the survival and growth of life on Earth, operates through the population-based process of reproduction. Inspired by this principle, this paper formally defines a newly emerging problem--the population-based evolution of Large Language Models (LLMs).

The authors propose a population-based evolution framework that involves training models to evolve based on the principles of natural selection. They demonstrate the effectiveness of their approach on several benchmark datasets, showing that their model outperforms existing models in terms of language understanding and generation.

In conclusion, the research related to Percy Liang has made significant progress in advancing the capabilities of language models. The development of more advanced and reliable language models has the potential to revolutionize various industries and applications, from customer service to healthcare. As the field continues to evolve, we can expect to see even more innovative approaches to language model development.
Q&A: Percy Liang - New Related Research

In our previous article, we explored some of the latest research related to Percy Liang, focusing on the development of more advanced and reliable language models. In this article, we will answer some of the most frequently asked questions about this research.

Q: What is the main goal of Percy Liang's research?

A: The main goal of Percy Liang's research is to develop more advanced and reliable language models that can understand and generate human-like language. His research focuses on improving the capabilities of language models in various tasks, such as language understanding, generation, and reasoning.

Q: What are some of the key challenges in developing language models?

A: Some of the key challenges in developing language models include:

  • Scalability: Language models need to be able to handle large amounts of data and scale to meet the demands of various applications.
  • Reliability: Language models need to be able to generate accurate and reliable responses to user queries.
  • Explainability: Language models need to be able to provide clear and transparent explanations for their responses.
  • Adaptability: Language models need to be able to adapt to changing user preferences and requirements.

Q: What are some of the key innovations in Percy Liang's research?

A: Some of the key innovations in Percy Liang's research include:

  • Risk-aware decision making: Percy Liang's research has introduced a novel approach to risk-aware decision making in language models, which involves training models to answer, refuse, or guess based on the risk level of the input.
  • Multidimensional consistency: Percy Liang's research has proposed a novel approach to multidimensional consistency in language models, which involves training models to generate consistent answers across different input variations.
  • Alignment control: Percy Liang's research has introduced a novel approach to alignment control in language models, which involves training models to adapt to different alignment preferences.
  • Audio reasoning: Percy Liang's research has proposed a novel approach to audio reasoning in language models, which involves training models to reason about audio inputs.

Q: What are some of the potential applications of Percy Liang's research?

A: Some of the potential applications of Percy Liang's research include:

  • Customer service: Percy Liang's research has the potential to revolutionize customer service by providing more accurate and reliable responses to user queries.
  • Healthcare: Percy Liang's research has the potential to improve healthcare by providing more accurate and reliable diagnoses and treatments.
  • Education: Percy Liang's research has the potential to improve education by providing more personalized and effective learning experiences.
  • Business: Percy Liang's research has the potential to improve business by providing more accurate and reliable market analysis and forecasting.

Q: What are some of the next steps in Percy Liang's research?

A: Some of the next steps in Percy Liang's research include:

  • Scaling up language models: Percy Liang's research will focus on scaling up language models to meet the demands of various applications.
  • Improving reliability: Percy Liang's research will focus on improving the reliability of language models by introducing novel approaches to risk-aware decision making and alignment control.
  • Exploring new applications: Percy Liang's research will explore new applications of language models, such as audio reasoning and multimodal understanding.

Q: How can readers stay up-to-date with Percy Liang's research?

A: Readers can stay up-to-date with Percy Liang's research by:

  • Following his publications: Readers can follow Percy Liang's publications on academic journals and conferences.
  • Attending his talks: Readers can attend Percy Liang's talks at conferences and workshops.
  • Joining his research group: Readers can join Percy Liang's research group to collaborate on his research projects.

We hope this Q&A article has provided readers with a better understanding of Percy Liang's research and its potential applications.