Enterprise AI Analysis
Evaluation of Question Answering Systems: Complexity of Judging a Natural Language
This article provides a comprehensive survey of evaluation scores for Question Answering (QA) systems, introducing a taxonomy that distinguishes between Human-Centric Evaluation Scores (HCES) and Automatic Evaluation Scores (AES). It details various evaluation scores, their pros and cons, and discusses benchmark datasets and QA paradigms, highlighting the complexities in assessing large language models (LLMs).
Executive Impact Summary
Here's what this deep dive means for your enterprise:
- QA systems evaluation is complex due to language diversity and task range.
- Traditional metrics often fail to capture semantic nuances; advanced measures like BERTScore are needed.
- KBQA systems rely on logical and grammatical precision for accurate knowledge base alignment.
- Human judgment is the gold standard but is expensive, subjective, and prone to bias.
- Learning-based metrics excel in semantic similarity but require extensive, often human-annotated, training data.
- LLMs introduce new evaluation challenges, surpassing traditional metrics and requiring nuanced human-based assessments.
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Total Pages Surveyed
200+
References Cited
9
Evaluation Score Properties
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The paper categorizes Question Answering (QA) systems into three main paradigms: Information Retrieval-Based QA (IRQA), Knowledge Base QA (KBQA), and Generative QA (GQA). Each paradigm has distinct evaluation considerations.
3
Primary QA Paradigms Discussed
| Paradigm | Description | Evaluation Focus |
|---|---|---|
| IRQA | Retrieves answers from documents, uses document-retriever and document-reader. | Recall, MRR (retriever); EM, F1-score, BERTScore (reader) |
| KBQA | Retrieves answers directly from a knowledge base via semantic parsing. | Logical and grammatical precision for alignment |
| GQA | Generates responses directly based on input context. | Lexical overlap (n-gram) for UAES; Semantic similarity for MTES |
General QA System Framework
QA Algorithms
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Knowledge Sources
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Question Types
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Answer Types
A hierarchical taxonomy of evaluation scores is introduced, classifying them into Human-Centric (HCES) and Automatic (AES), with AES further split into Untrained (UAES) and Machine-Trained (MTES). Each type has distinct characteristics and applications.
50+
New Scores Introduced Post-2014
| Category | Pros | Cons |
|---|---|---|
| HCES |
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| S-UAES |
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| A-UAES |
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| MTES |
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ADEM in Dialogue Systems
The Automatic Dialogue Evaluation Model (ADEM) addresses limitations of word-overlap metrics by learning distributed representations of context, response, and reference. It uses RNN encoders and supervised learning to predict human-like scores, demonstrating high correlation with human judgment in dialogue evaluation, as shown in Table 9 of the paper. This allows for more nuanced semantic evaluation beyond simple word matching.
4.726
ADEM Score Example 1
4.201
ADEM Score Example 2
5.0
ADEM Score Example 3
The evaluation of QA systems faces inherent complexities, particularly with the advent of Large Language Models (LLMs). Challenges include capturing semantic nuances, the cost and subjectivity of human judgment, and the limitations of both word-overlap and learning-based scores with out-of-distribution data.
18%
Incorrect GPT-4 Judgments (LLaMA Eval)
| Score Type | Key Drawback |
|---|---|
| Word-overlap |
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| Learning-based |
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| Pre-trained Model Based |
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| Human Judgment |
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LLM Evaluation Challenges
LLMs like GPT-3/4 introduce new complexities. Traditional metrics often fall short for open-ended generation, necessitating human assessment despite its drawbacks. A study found GPT-4 made 18% incorrect judgments when evaluating LLaMA on Natural Questions due to inadequate knowledge, highlighting the need for robust, multi-faceted evaluation techniques.
900M+
LLM Parameters
8K+
Conversations (CoQA)
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Strategic AI Implementation Roadmap
Our phased approach ensures a smooth transition and maximum value realization for your enterprise.
Phase 1: Discovery & Strategy
Assess current QA processes, define objectives, and tailor AI strategy. (2-4 Weeks)
Phase 2: Pilot & Development
Develop and integrate AI QA solution for a pilot group. (8-12 Weeks)
Phase 3: Iteration & Expansion
Refine the system based on feedback and scale across the enterprise. (6-10 Weeks)
Phase 4: Optimization & Monitoring
Continuous monitoring, performance tuning, and new feature integration. (Ongoing)
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