Search Results for author:
Found 16 papers, 11 papers with code
Med-R$^2$: Crafting Trustworthy LLM Physicians through Retrieval and Reasoning of Evidence-Based Medicine
In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios.
Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge
Specifically, the VE's representation of visual information may not fully align with LLM's cognitive framework, leading to a mismatch where visual features exceed the language model's interpretive range.
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs
Despite their potential, existing work mainly focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains.
Baichuan Alignment Technical Report
Baichuan-Instruct is an internal model, while Qwen2-Nova-72B and Llama3-PBM-Nova-70B are instruct versions of the Qwen2-72B and Llama-3-70B base models, optimized through Baichuan Alignment.
Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning
While current research on function calling by LLMs primarily focuses on single-turn interactions, this paper addresses the overlooked necessity for LLMs to engage in multi-turn function calling--critical for handling compositional, real-world queries that require planning with functions but not only use functions.
FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs' Responsiveness to Human Feedback
Human feedback is crucial in the interactions between humans and Large Language Models (LLMs).
Baichuan-Omni Technical Report
The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpart.
Ranked #20 on
Visual Question Answering
on MM-Vet
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains.
Data Proportion Detection for Optimized Data Management for Large Language Models
Large language models (LLMs) have demonstrated exceptional performance across a wide range of tasks and domains, with data preparation playing a critical role in achieving these results.
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
Through extensive experimental analysis, we identified three key challenges in designing effective data management strategies that enable the model to achieve long-context capability without sacrificing performance in other tasks: (1) a shortage of long documents across multiple domains, (2) effective construction of context windows, and (3) efficient organization of large-scale datasets.
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions.
SysBench: Can Large Language Models Follow System Messages?
Finally, we conduct extensive evaluation across various existing LLMs, measuring their ability to follow specified constraints given in system messages.
MathScape: Evaluating MLLMs in multimodal Math Scenarios through a Hierarchical Benchmark
To address this gap, we proposed MathScape, a new benchmark that emphasizes the understanding and application of combined visual and textual information.
CFBench: A Comprehensive Constraints-Following Benchmark for LLMs
The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications.
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
