RAG Challenges and Solutions

This post is based on this excellent article that discusses the different ways that RAG can fail during the stages of retrieval of data, augmentation of this information, and generation process. The purpose of this post is to expand on it, provide my own take on it and propose different ways for tackling these challenges.

Retrieval

• Addressing Semantic Ambiguity:
  • Problem: Dealing with semantic ambiguity due to homonyms and polysemy presents a significant challenge. LLMs and RAG, often struggle to differentiate subtle semantic nuances, leading to potentially irrelevant outputs.
  • Solution: Integrate Named Entity Recognition (NER) models, a broad list of these models can be found in Hugging Face. Some good examples are bet-base-NER, and (GLiNER)[https://github.com/urchade/GLiNER]. These models help clarify the meaning of entities within text, thereby giving a solution to improve queries.
• Measuring Similarity and Vector Dimensionality:
  • Problem: As dimensionality grows, similarity measurement of vectors may become more complex. The accurate measurement of similarity between vectors is crucial for optimizing AI driven information retrieval systems.
  • Solution: Commonly used metrics for this purpose include Cosine Similarity, Euclidean Distance, and Maximum Inner Product. Proper evaluation of the impact of using different distance metrics is of extreme importance. It may even be possible to use different ones at different stages of your system. Therefore, improving these systems involves exploring innovative techniques to mitigate these limitations while maximizing semantic fidelity.

  While Cosine similarity offers advantages like being invariant to vector magnitude and suitable for analyzing high-dimensional data, it tends to overlook magnitude differences, potentially affecting the accuracy of semantic assessments. On the other hand, metrics such as Euclidean distance capture both magnitude and direction differences but are prone to issues like the curse of dimensionality and outliers in high-dimensional spaces.

• Enhancing Sparse Retrieval with Context-Aware Conversations:
  • Problem: Sparse retrieval, which involves the challenge of identifying relevant information scattered across extensive datasets.
  • Solution: Contextualizing queries within ongoing conversations. Aligning the context with the search enhances the relevance of retrieved documents. However, it’s crucial to be mindful of context limitations and avoid passing unlimited context. The correlation between the new query and the previous context can help determine whether the prior context and its relevant answer are linked to the new query.

Augmentation

• Integration and Ranking of Retrieved Information:
  • Problem: When multiple sources of information are retrieved and they are not integrated properly, the output will be disjointed, lack coherence with the task, may be repetitive, and may not assign proper importance weights for the different documents.
  • Solution: Smooth integration, taking into account the ultimate task at hand, is crucial to avoid the following: Analyzing documents to identify crucial themes; Utilizing summarization techniques; Establishing a ranking system based on relevance and user preferences; Incorporating algorithms to detect and handle repetitive content; Evaluating diversity to mitigate redundancy.

Generation

• LLM-related Challenges:
  • Problem: Hallucinations in LLMs may produce factual inconsistencies and fabrications, posing a threat to the integrity of generated content. Another problem is that each LLM has a different constraint on the limit of context length that can be provided. Limiting the amount of information that can be provided alongside the model.
  • Solution: To mitigate hallucinations in the model, employing Advanced Prompting techniques is beneficial. These methods help guide the model to better comprehend the task and desired outputs. Examples include:
    • Explicitly instructing the model within the “system prompt” to avoid undesirable actions such as spreading false or unverifiable information.
    • Few-shot prompting, which reduces hallucinations by providing a small number of specific examples to steer the model’s responses.
    • Chain-of-thought prompting, which directs the model to generate reasoning steps before providing final answers. This can be achieved by instructing the model to do a step-by-step reasoning or providing specific reasoning examples.
    • Utilization of external tools such as database calls, API invocations, scripts for data processing, or separate models tailored to specific tasks.
• Over-generalization and Lack of Depth:¹
  • Problem: When dealing with multiple retrieved contexts, RAG may produce generic responses rather than specific, detailed answers tailored to the query.
  • Solution: Expand corpora by aggregating documents from diverse sources to increase likelihood of coverage. Design modular architectures to update knowledge sources without full re-deployment.
• Lack of interpretability:
  • Problem: Lack of insight into the rationale behind the generated text results in incomplete explanations for the responses. This lack of transparency undermines adoption and trust, and constraints the ability to debug and improve.
  • Solution: System design can explicitly track evidence and explanations as structured chains.

References

1. https://medium.com/@bijit211987/top-rag-pain-points-and-solutions-108d348b4e5d ↩