Fine-tuning Smaller Language Models for Question Answering over Financial Documents (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent research has shown that smaller language models can acquire substantial reasoning abilities when fine-tuned with reasoning exemplars crafted by a significantly larger teacher model. |
| Approach: | They propose to fine-tune several smaller model to generate programs that encode the required financial reasoning and calculations. |
| Outcome: | The proposed model outperforms the teacher model in the financial domain by adjusting the entity extraction for the specific data format. |
Similar Papers
Large Language Models Are Reasoning Teachers (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that chain-of-thought (CoT) prompting can elicit language models to solve complex reasoning tasks step-by-step. |
| Approach: | They propose a method that uses large model samples as reasoning teachers to fine-tune smaller models. |
| Outcome: | The proposed method outperforms prompt-based methods and the teacher model in many tasks and extends it by leveraging the teacher's ability to generate multiple rationales for each original sample. |
Synthesizing question answering data from financial documents: An End-to-End Multi-Agent Approach (2026.eacl-industry)
Copied to clipboard
| Challenge: | Large language models excel at financial reasoning but their deployment for enterprise use cases remains costly and often constrained by latency, privacy, and regulatory requirements. |
| Approach: | They propose a pipeline that extracts and selects relevant content from unstructured financial documents and generates QA pairs from the selected content for SLM fine-tuning. |
| Outcome: | The proposed model outperforms models trained on previous manual models and achieves competitive in-distribution performance. |
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated remarkable progress in reasoning, but are resource-intensive and require large curated datasets. |
| Approach: | They propose a framework that leverages the behavioral divergence between a stronger expert model and a weaker amateur model. |
| Outcome: | The proposed framework improves accuracy by up to 28.1% while reducing time consumption by 90% and tuning token usage by 99%. |
Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
Copied to clipboard
| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
| Approach: | They propose to review parameter-efficient fine-tuning techniques that lower training and deployment costs and domain and cross-lingual adaptation methods for both encoder and decoder models. |
| Outcome: | The proposed techniques lower training and deployment costs, domain and cross-lingual adaptation methods, and model specialization strategies. |
Evaluating Parameter-Efficient Finetuning Approaches for Pre-trained Models on the Financial Domain (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large-scale language models with millions, billions, or trillions of trainable parameters are becoming increasingly popular. |
| Approach: | They compare performance of financial BERT-like models to their fully fine-tuned counterparts by using parameter-efficient tuning methods. |
| Outcome: | The proposed approaches match full fine-tuning performance on common NLP tasks, but are less studied in finance. |
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing frameworks for guiding a language model in reasoning tasks are limited by their tendency to generate low-quality rationales that are repetitive and vacuous. |
| Approach: | They propose a framework that leverages a lightweight language model for guiding a black-box large LM in reasoning tasks. |
| Outcome: | The proposed framework outperforms baselines in answer prediction accuracy. |
Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning (2025.coling-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks. |
| Approach: | They propose a new reasoning strategy Solution Guidance (SG) and a plug-and-play training paradigm Solution-Guidance Fine-Tuning (SGFT) which focuses on problem understanding and decomposition at the semantic and logical levels, rather than specific computations. |
| Outcome: | The proposed reasoning strategy Solution Guidance (SG) and plug-and-play training paradigm Solution-Guidance Fine-Tuning (SGFT) improves the reasoning capabilities of small language models on various reasoning tasks. |
There’s No Such Thing as Simple Reasoning for LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work has focused on relatively complex “many-hop” reasoning problems. |
| Approach: | They analyse the performance of fine-tuned LLMs on simple reasoning problems . they find the models remain highly brittle, being susceptible to seemingly innocent perturbations . |
| Outcome: | The proposed models fail on simple reasoning problems, but are highly brittle . they are susceptible to seemingly innocent perturbations, such as adding duplicates to the set of premises and shuffling the order in which the premises are presented. |
What Factors Affect LLMs and RLLMs in Financial Question Answering? (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on large language models and reasoning large language model (RLLMs) however, there are few studies that explore what methods can fully unlock the performance of LLMs and RLLM in the financial domain. |
| Approach: | They examine the effects of prompting methods, agentic frameworks, and multilingual alignment methods on financial question-answering tasks. |
| Outcome: | The results show that prompting methods and agent frameworks improve LLMs' performance . the authors suggest that these frameworks can be used to enhance LLM performance if they are implemented in financial domains. |
Unveiling the Generalization Power of Fine-Tuned Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, but the comprehensive effects of fine-tuning on the LLMs’ generalization ability are not fully understood. |
| Approach: | They conduct extensive experiments across five distinct language tasks on different datasets to investigate whether fine-tuning affects the generalization ability intrinsic to LLMs. |
| Outcome: | The proposed model can generalize to different domains and tasks by integrating the in-context learning strategy during fine-tuning on generation tasks. |