# First Token Probability Guided RAG for Telecom Question Answering

arXiv: https://arxiv.org/abs/2501.06468 · HTML: https://mlsanity.com/paper/2501.06468/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- First posted on arXiv 11 Jan 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Built on by (most cited first, 4 of 4)

- [Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoring](https://mlsanity.com/paper/2507.17442/) (2025)
- [QoEReasoner: An Agentic Reasoning Framework for Automated and Explainable QoE Diagnosis in RANs](https://mlsanity.com/paper/2606.01925/) (2026)
- [ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval](https://mlsanity.com/paper/2608.12720/) (2026)
- [Optimizing Denoising Trajectories in dLLMs: A Lightweight Evolutionary Heuristic Approach](https://mlsanity.com/paper/2609.26052/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have garnered significant attention for their impressive general-purpose capabilities. For applications requiring intricate domain knowledge, Retrieval-Augmented Generation (RAG) has shown a distinct advantage in incorporating domain-specific information into LLMs. However, existing RAG research has not fully addressed the challenges of Multiple Choice Question Answering (MCQA) in telecommunications, particularly in terms of retrieval quality and mitigating hallucinations. To tackle these challenges, we propose a novel first token probability guided RAG framework. This framework leverages confidence scores to optimize key hyperparameters, such as chunk number and chunk window size, while dynamically adjusting the context. Our method starts by retrieving the most relevant chunks and generates a single token as the potential answer. The probabilities of all options are then normalized to serve as confidence scores, which guide the dynamic adjustment of the context. By iteratively optimizing the hyperparameters based on these confidence scores, we can continuously improve RAG performance. We conducted experiments to validate the effectiveness of our framework, demonstrating its potential to enhance accuracy in domain-specific MCQA tasks.