Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework (2410.18653v3)

Abstract: Open-ended text generation has become a prominent task in natural language processing due to the rise of powerful (large) LLMs. However, evaluating the quality of these models and the employed decoding strategies remains challenging due to trade-offs among widely used metrics such as coherence, diversity, and perplexity. This paper addresses the specific problem of multicriteria evaluation for open-ended text generation, proposing novel methods for both relative and absolute rankings of decoding methods. Specifically, we employ benchmarking approaches based on partial orderings and present a new summary metric to balance existing automatic indicators, providing a more holistic evaluation of text generation quality. Our experiments demonstrate that the proposed approaches offer a robust way to compare decoding strategies and serve as valuable tools to guide model selection for open-ended text generation tasks. We suggest future directions for improving evaluation methodologies in text generation and make our code, datasets, and models publicly available.

• The paper introduces a multicriteria framework that combines Q*Text and partial orderings to evaluate generated text, achieving rankings aligned with human judgment.
• It employs pairwise comparisons using the Bradley-Terry model and union-free generic depth to balance key metrics such as coherence, diversity, and perplexity.
• Experiments across six models and three datasets demonstrate that moderate decoding hyperparameters, like balanced contrastive search settings, optimize generation quality.

Okay, I need to summarize the paper "Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework" (2410.18653). Let me start by understanding the abstract and the main sections.

The paper addresses the challenge of evaluating open-ended text generation from LLMs, where traditional metrics like coherence, diversity, and perplexity often conflict. The authors propose a multicriteria evaluation framework using partial orderings and a new summary metric called Q*Text. They also compare these methods with human judgments.

Looking at the introduction, the problem is that existing metrics don't provide a comprehensive assessment, and decoding methods trade off between different qualities. The authors mention using benchmarking approaches based on partial orderings (like the Bradley-Terry model and union-free generic depth) and introduce Q*Text to balance metrics.

In the experimental setup, they evaluate six models (GPT2-XL, Mistral, Llama, etc.) across three datasets (Wikinews, WikiText, BookCorpus) using five decoding strategies (beam search, contrastive search, temperature sampling, top-k, top-p) with various hyperparameters. Metrics include diversity, coherence, and generation perplexity.

The benchmarking section explains using the Bradley-Terry model for pairwise comparisons and the ufg depth for partial orders. The Q*Text metric combines normalized and penalized scores of the three metrics using harmonic mean. Results show that balanced decoding strategies (e.g., contrastive search with moderate α and k) perform best, aligning with human preferences. Beam search tends to have low diversity, while some hyperparameters lead to incoherent text.

The conclusion emphasizes the framework's practicality for model selection and the trade-offs between different evaluation approaches. The authors provide code and datasets for reproducibility.

Now, structuring the summary: start with the problem, present the proposed methods (multicriteria benchmarking, Q*Text), outline the experimental setup, key results, and implications. Highlight the implementation details, metrics, and how practitioners can choose methods based on their needs. Mention the alignment with human evaluation and the provided resources.

This paper introduces a comprehensive framework for evaluating open-ended text generation models, addressing the challenge of balancing conflicting quality metrics like coherence, diversity, and generation perplexity. The authors propose two complementary approaches and validate them through extensive experiments across 6 LLMs, 3 datasets, and 59 hyperparameter configurations.

Key Components & Implementation:

1. Multicriteria Benchmarking:
   • Partial Order Ranking: Uses union-free generic (ufg) depth to handle incomparable method performances
   • Bradley-Terry Model: Creates total rankings through pairwise comparisons

     # Pseudo-code for Bradley-Terry worth calculation def calculate_worth(pairwise_wins): # Initialize parameters params = np.ones(n_methods) for _ in range(iterations): for i,j in all_pairs: p_i_beats_j = sigmoid(params[i] - params[j]) params[i] += learning_rate * (actual_wins[i,j] - p_i_beats_j) params[j] -= learning_rate * (actual_wins[i,j] - p_i_beats_j) return softmax(params)

2. Q*Text Metric:
   • Normalizes and combines three core metrics:
     • Coherence (log-likelihood of generated text)
     • Diversity (n-gram variation)
     • Generation Perplexity
   • Applies sigmoid-based regularization to prevent metric dominance

$$Q*Text = 3 \left( \frac{1}{\text{Coherence} + \text{Diversity} + \text{Perplexity}} \right)^{-1}$$

Practical Findings:

• Optimal decoding strategies combine moderate hyperparameters:
  • Contrastive search with α=0.4-0.6 and k=5-15
  • Temperature sampling >0.7
  • Top-p sampling >0.8
• Beam search underperforms due to low diversity
• Larger models (Mistral 7B, Falcon2 11B) generally outperform smaller ones, though proper decoding configuration can mitigate differences

Implementation Considerations:

• Computational cost: 1.8M generated texts analyzed
• Trade-offs:
  • Bradley-Terry provides total order but ignores incomparability
  • ufg depth preserves uncertainty at higher computational cost
  • Q*Text offers single metric but requires careful calibration

Alignment with Human Evaluation:

The framework correlates with human preferences (from prior studies (Arias et al., 8 Oct 2024)), particularly for:

• Balanced contrastive search configurations
• Nucleus sampling (p=0.9)
• Avoidance of extreme hyperparameters that cause repetition or incoherence

Recommendations for Practitioners:

1. Use Q*Text for quick comparisons requiring a single metric
2. Apply Bradley-Terry for strict rankings when metrics agree
3. Employ ufg depth when preserving uncertainty is crucial
4. Avoid beam search widths >20 and contrastive search α>0.8

The authors provide full implementation code and datasets at GitHub repository, enabling direct application of these evaluation methods to new models and decoding strategies.