| Challenge: | Existing studies have overlooked the impact of hyperparameters on table understanding abilities . authors show that smaller learning rates and fewer training instances can enhance table understanding while preserving general capabilities. |
| Approach: | They propose a hyperparameter-based instruction-tuned model for table-related tasks that improves out-of-domain table understanding ability and general capabilities. |
| Outcome: | The proposed model outperforms existing models on table-related tasks while maintaining strong out-of-domain generalization and general capabilities. |
Similar Papers
What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects (2026.findings-eacl)
Copied to clipboard
Naihao Deng, Sheng Zhang, Henghui Zhu, Shuaichen Chang, Jiani Zhang, Alexander Hanbo Li, Chung-Wei Hang, Hideo Kobayashi, Yiqun Hu, Patrick Ng
| Challenge: | a series of paradigm shifts have come with distinct characteristics and challenges associated with table modeling. |
| Approach: | They propose to replicate four table LLMs by instruction-tuning three foundation models on four existing datasets. |
| Outcome: | The results show that base model choice plays a more dominant role than training data itself. |
Call for Rigor in Reporting Quality of Instruction Tuning Data (2025.acl-short)
Copied to clipboard
| Challenge: | Instruction tuning is crucial for adapting large language models (LLMs) to user intentions. |
| Approach: | They propose to use hyperparameters for training models that are often selected arbitrarily without adequate justification to make arbitrary conclusions. |
| Outcome: | The results show that arbitrary hyperparameter decisions can make any arbitrary conclusion. |
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks is suboptimal. |
| Approach: | They propose a generator-validator paradigm to iteratively generate-then-validate training data from language models to fine-tune stronger Table-Specialist models that can specialize in a given task, without using manually-labeled data. |
| Outcome: | The proposed model outperforms vanilla language models on diverse table tasks and can match or surpass GPT-4 level quality. |
Large Language Models Can Not Perform Well in Understanding and Manipulating Natural Language at Both Character and Word Levels? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) still exhibit significant deficiencies in basic language understanding and manipulation. |
| Approach: | They propose a bilingual benchmark to assess the performance of Large language models . they use a set of 15 simple text editing tasks to examine their capabilities . |
| Outcome: | The proposed benchmark aims to assess the performance of Large language models in basic language tasks. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca (2024.findings-eacl)
Copied to clipboard
| Challenge: | Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. |
| Approach: | They employ a dataset and machine translations of it to form multilingual data and use it to tune LLMs. |
| Outcome: | The proposed model is on par or better than a model for each language, and multilingual tuning with downsampled data is as powerful and robust. |
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that instruction tuning can be a data-efficient method for transforming large language models into generalist models, but their performance lags behind specialist models trained exclusively for specific tasks. |
| Approach: | They propose to incorporate broadcoverage generalist instruction tuning into large language models to build a specialist model by incorporating task specificity and skill requirements. |
| Outcome: | The proposed method improves model performance when task coverage is broad and when training data is limited. |
Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
Copied to clipboard
| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
| Outcome: | The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance. |
TableLlama: Towards Open Large Generalist Models for Tables (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for interpreting, augmenting, and querying semi-structured tables require pretraining on tables or special model architecture design. |
| Approach: | They construct a dataset with a variety of tables and tasks for instruction tuning and evaluating LLMs. |
| Outcome: | The proposed model achieves comparable or better performance on 7 out of 8 in-domain tasks compared with the base model on 6 out-of-domain datasets. |
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)
Copied to clipboard
| Challenge: | Instruction tuning is critical to large language models but its success heavily relies on the training data quality. |
| Approach: | They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data. |
| Outcome: | The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data. |