FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs' Responsiveness to Human Feedback

## Abstract

Abstract Human feedback is crucial in the interactions between humans and Large Language Models (LLMs). However, existing research primarily focuses on benchmarking LLMs in single-turn dialogues. Even in benchmarks designed for multi-turn dialogues, the user inputs are often independent, neglecting the nuanced and complex nature of human feedback within real-world usage scenarios. To fill this research gap, we introduce FB-Bench, a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback in real-world usage scenarios. Drawing from the two main interaction scenarios, FB-Bench comprises 734 meticulously curated samples, encompassing eight task types, five deficiency types of response, and nine feedback types. We extensively evaluate a broad array of popular LLMs, revealing significant variations in their performance across different interaction scenarios. Further analysis indicates that task, human feedback, and deficiencies of previous responses can also significantly impact LLMs’ responsiveness. Our findings underscore both the strengths and limitations of current models, providing valuable insights and directions for future research. Both the toolkits and the dataset of FB-Bench are available at https://github.com/PKU-Baichuan-MLSystemLab/FB-Bench .

## 1 Introduction

Equipped with advanced intelligence and formidable processing capabilities, large language models (LLMs) have demonstrated substantial potential extensive potential in seamless interaction with human users and in assimilating real-time human feedback during inference processes []. This human-LLM synergy can be mutually beneficial, breaking through the limitations inherent to each side [, ] and has been applied in many domains [, , , ].

As a main component of human-LLM synergy, human feedback acts as a response to prior model outputs, serving as a guiding force that directs LLMs towards the desired outcomes []. In practical applications, LLMs often need to iteratively adjust their responses based on user feedback in multi-turn dialogues to fulfill user needs. Effective feedback can enhance the quality of responses, while ineffective feedback may mislead LLMs. A robust LLM should leverage appropriate feedback and remain undisturbed by inappropriate feedback.
However, evaluating the responsiveness of LLMs to human feedback within multi-turn dialogues presents a significant challenge, as these models exhibit divergent behaviors compared to single-turn dialogues. As illustrated in Figure 1, LLMs that perform well in single-turn interactions may struggle to incorporate user feedback effectively. Conversely, models that may not excel in single-turn scenarios could excel in correcting their previous errors by skillfully leveraging user feedback to improve responses.

Figure: Figure 1: LLMs proficient in single-turn interactions might not handle user feedback well (left), while those not great at single-turn can excel in correcting their previous errors by using feedback effectively (right).
Refer to caption: x1.png

Despite the advancement in evaluating LLMs, there remains a lack of a systematic benchmark for evaluating their responsiveness to various human feedback under real-world usage scenarios.
Most existing benchmarks [, , , ] assess LLMs within single-turn scenarios, which is naturally incapable of evaluating LLMs’ responsiveness to human feedback.
Although there are benchmarks designed for multi-turn dialugues [, , ], the user inputs are often independent in a dialogue, lacking feedback towards to the previous LLM output.
While several benchmarks incorporating feedback exist [, , ], they typically assess LLMs on a singular task or within a specific domain, and the feedback is often not generated by real humans, failing to capture the complexity and diversity of human-LLM interaction.

In this work, we introduce FB-Bench, a fine-grained multi-task benchmark designed to evaluate LLMs’ responsiveness to various human feedback within real-world usage scenarios. Drawing from the interaction scenarios of error correction and response maintenance, FB-Bench organizes a three-tier hierarchical taxonomy that encapsulates the fundamental elements of human-LLM interaction: user queries, model responses, and user feedback, as illustrated in Figure 2.
It includes eight popular task types, five types of deficiencies in previous model outputs, and nine types of user feedback.

After meticulous curation, we collect 734 samples in FB-Bench, each consisting of a task-oriented user query, a preset model response, human feedback, a human-curated reference follow-up response, and a weighted checklist for evaluation.
To precisely assess the performance of LLMs in a detailed manner, we employ GPT-
4o to act as a judge, scoring the model-generated follow-up responses based on the weighted checklist and a human-curated reference follow-up response.

We conducted extensive experiments across a broad spectrum of popular LLMs. The results indicate significant variations in performance between error correction and response maintenance scenarios.
We further analyze the impact of interaction scenarios, tasks, feedback, and previous responses’ deficiencies on LLMs’ responsiveness. These analyses reveals that

- •
Most LLMs exhibit superior performance in error correction compared to response maintenance.
- •
Advanced LLMs show similar performance on each task in error correction. In contrast, their performance varies widely in response maintenance.
- •
Directly pointing out errors significantly aids LLMs in enhancing the quality of responses, whereas providing feedback with fabricated credentials or expertise often misleads them.
- •
Advanced LLMs outperform less capable LLMs in addressing all categories of deficiencies, especially in logic and following instructions.

To summarize, our work makes the following contributions:

- •
New perspective. We develop a three-tier hierarchical taxonomy that encapsulates the fundamental elements of human-LLM interactions, focusing primarily on two main interactive scenarios: error correction and response maintenance.
- •
New benchmark. We introduce FB-Bench, the first systematic benchmark for comprehensively evaluating LLMs’ responsiveness to human feedback across a spectrum of real-world, multi-task scenarios.
- •
More fine-grained evaluation. We develop a framework that employs a weighted, sample-specific checklist and a human-curated follow-up response to facilitate a fine-grained evaluation of each sample.
- •
New findings. We perform a comprehensive evaluation of 31 different LLMs using FB-Bench, uncovering a significant performance discrepancy between error correction and response maintenance. We further analyze the factors that may impact the responsiveness of LLMs and provide valuable insights and directions for future research.

## 2 FB-Bench

In this section, we first outline the design logic behind FB-Bench in § 2.1 and  § 2.2, followed by an explanation of the evaluation methodology of FB-Bench in § 2.3. Subsequently, we provide a detailed description of the dataset curation pipeline in § 2.4 and finally present a statistical analysis of the dataset in §2.5.

Figure: Figure 2: Overview of FB-Bench. (1)Data Curation: A human-LLM synergy pipeline for mining the target data from real-world usage scenarios and improving their quality and diversity. (2)Three-tier Hierarchical Taxonomy: Comprising 8 popular task types, 2 deficiency types and 9 feedback types, derived from two interaction scenarios. (3)Auto-Evaluation: An LLM-as-a-Judge framework to automatically evaluate LLM’s response with a weighted checklist and a reference follow-up response.
Refer to caption: x2.png

### 2.1 Interaction scenario

In practical applications, error correction and response maintenance are two prevalent and significant scenarios. These scenarios encapsulate the essential dynamics between users and models, underscoring the importance of models’ ability to adapt and respond effectively to user feedback.

Error Correction: Users may pose a query and find the model’s response either objectively incorrect or unsatisfactory. Consequently, they provide feedback, expecting the model to acknowledge its response’s inadequacies and offer an improved version.

Response Maintenance: Alternatively, when a user’s query receives an objectively correct or satisfactory response from the model, they might still engage in feedback. This could be to either reaffirm or challenge the provided answer, aiming to verify the correctness and reliability of the information. The expectation is that the model will sustain its initial response upon receiving this feedback.

### 2.2 Hierarchical data taxonomy

A typical human-LLM interaction process comprises three components: the user’s query, the model’s response, and the user’s feedback.
To ensure comprehensive coverage of various potential interaction scenarios and interaction types, we develop an extensive three-tier hierarchical taxonomy from the perspective of these three components.

#### 2.2.1 Query task

From the perspective of user queries, the diversity of interactions primarily stems from the task type associated with each query. Therefore, we select eight popular tasks to encompass most real-world usage scenarios. To enhance the diversity of queries further, we categorize the eight tasks into twenty-four subtasks, as detailed in Appendix A.1.1.

Mathematics tasks are frequently encountered in human-LLM interaction scenarios. Given the complexity of these problems, models often fail to provide accurate answers on their first attempt, necessitating collaboration between humans and models to resolve complex issues.

Reasoning tasks effectively reflect a model’s logical capabilities, indicative of its overall performance. Strong logical abilities enable the model to excel in other complex tasks, making it a vital component of human-LLM interaction.

Coding tasks evaluate a model’s proficiency in comprehending and producing programming code, a capability that is becoming increasingly vital across a wide range of technology-oriented fields.

Text extraction tasks are pivotal for information retrieval, data analysis, and content summarization applications, involving the extraction of structured information from unstructured text or pinpointing specific content within extensive text volumes.

Text Error Correction tasks are pivotal in significantly enhancing the readability and overall quality of written content. By fixing errors from typos to grammar, these tasks make text accurate and clear, highlighting their key role in keeping written communication professional and intact.

Text creation tasks not only test the model’s creativity and understanding but also play a crucial role in aiding people to express ideas more effectively and innovatively, enriching communication across various fields.

Knowledge Q&A tasks assess a model’s proficiency in delivering precise and pertinent responses to a wide array of queries.

Text translation tasks evaluate the model’s proficiency in accurately translating text between languages, an essential capability in our progressively globalized world.

#### 2.2.2 Model response

From the perspective of the model’s response, it is either objectively correct or satisfies the user in response maintenance scenarios. In error correction scenarios, to enable more fine-grained research, we further categorize the deficiencies of model responses into the following five types:

- •
Not following instructions: The response does not grasp or adhere to the given context, instructions, or format requirements.
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Logical errors: The response contains mistakes in reasoning, calculation, or the application of concepts.
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Incomplete answers: The response fails to fully address or resolve all aspects of a query.
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Factual errors: The response includes incorrect or outdated information, encompassing grammatical and technical inaccuracies.
- •
Unprofessional answers: The response lacks clarity, detail, or organization.

#### 2.2.3 User feedback

From the perspective of user feedback, the interaction between humans and LLMs can be significantly influenced by the nature of the user feedback provided.
We design a total of nine distinct types of feedback, comprising six for error correction and four for response maintenance, with one type overlapping between error correction and response maintenance.
Table 1 provides a brief one-sentence description for each feedback within error correction and response maintenance scenarios.

**Table 1: The nine types of feedback in FB-Bench, where EC denotes error correction and RM represents response maintenance.**
| Feedback | Scenario | Description |
| --- | --- | --- |
| Pointing Out Errors | EC | Highlight specific inaccuracies or absurdities in the model’s output |
| Clarifying Intent | EC | Refine queries to guide the model towards more accurate and relevant responses. |
| Raising Objections | EC | Encourage the exploration of superior alternative solutions. |
| Detailed Explanation | EC | Request further information or a deeper understanding of the model’s response. |
| Hinting Guidance | EC | Guide the model at key points in problem-solving. |
| Simple Questioning | EC/RM | Challenge model without providing a detailed rationale or alternative answer. |
| Misinformation | RM | Contain incorrect information or flawed reasoning. |
| Credibility Support | RM | Challenge model’s response with fabricated authority or expertise. |
| Unreasonable Requests | RM | Propose demands or queries that fall outside ethical or common-sense boundaries. |

### 2.3 Evaluation Protocol

Inspired by DRFR [], we evaluate the quality of follow-up responses by decomposing the evaluation criteria into a series of criteria that constitute a checklist.
Considering the efficiency and capabilities of LLMs, we adopt the LLM-as-a-Judge framework to evaluate the quality of response as previous works [, ].
Specifically, we employ GPT-4o to act as a judge, scoring the model-generated follow-up responses based on the checklist and a human-curated reference follow-up response.

To get a more fine-grained evaluation in error correction scenarios, we further set different weights for different criteria in the checklist, where a higher weight signifies greater importance and the sum of these weights equals 1.
If the response meets any criterion in the checklist, it receives the corresponding points. For $i$-th sample in error correction scenarios,

$$ $score_{i}=\sum\limits_{j=1}^{n}w_{i,j}r_{i,j}$ $$

where $w_{i,j}$ is the weight of $j$-th criterion, $r_{i,j}\in[0,1]$ denotes whether the $j$-th criterion within $i$-th sample is met.

In response maintenance, since the model has already provided the correct answer in the previous round, it will get credits if it maintains its stance and is not swayed by the user’s instructions. That’s to say, meeting any criterion in the checklist yields a score of 1.

$$ $score_{i}=\begin{cases}1,&\forall{r_{i,j}=1},j\in[1,n]\\ 0,&\text{otherwise}.\end{cases}$ $$

### 2.4 Dataset Curation

Each sample in FB-Bench mainly contains a task-oriented user query, a preset model response, human feedback, a human-curated reference follow-up response, and a weighted checklist for evaluation.

The example can be found in Appendix A.1.3. The detailed construction pipeline is described as follows.

##### Collection

To ensure the diversity and authenticity of user queries, we mine relevant data from two primary sources: an online chat service and human preference data, both derived from real-world usage scenarios.
For error correction data, we employ heuristic rules to identify target data within the online chat service and select the response with the lowest score from human preference data. For response maintenance data, we adopt an opposite strategy to filter the target data from the two data sources.
After gathering the above data, we perform deduplication and anonymization, and categorize them into predefined tasks and subtasks using an in-house model to construct high task diversity data.

##### Annotation

Although mined data exhibit high task diversity, the feedback from most users is usually simple and homogenous. To improve the quality and variety of user feedback and to supply essential elements for further analysis, we invite annotators to label data with finer granularity. Considering the excellent performance of LLMs in aiding humans to generate comprehensive critiques and reduce hallucination rates [], we have annotators collaborate with GPT-4 to enhance the quality and efficiency of the annotation process. Initially, we utilize GPT-4 to ascertain the cause of dissatisfaction when a model’s response does not meet the user’s expectations and then simulate a user providing detailed feedback. Subsequently, GPT-4 is tasked with generating a reference follow-up response and a weighted checklist to facilitate the evaluation.
Finally, the annotators act as the reviewers to refine all pre-annotated elements of each sample.

##### Post-Filtering

To enhance distinguishment in scores among LLMs, we utilize three models: Mistral-7B-Instruct-v0.3 [], Phi-3-mini-4k-instruct [], and Yi-1.5-9B-Chat [] as difficulty filters in our dataset curation pipeline. Specifically, we benchmark these models using this dataset, analyze their responses, and score them by GPT-4o. Finally, we discard samples for which all three models achieved full scores.

### 2.5 Dataset Statistics

After meticulous curation, we collect 734 high-quality, diverse, and complex samples.
The distributions of tasks, deficiencies in previous model responses within error correction scenarios, and user feedback within both error correction and response maintenance scenarios are all shown in Figure 3.
More detailed statistics can be found in Appendix A.1.2

Figure: Figure 3: FB-Bench Statistics.
Refer to caption: x3.png

## 3 Experiments

### 3.1 Experimental Setup

Models. Given the considerations of performance, size and the popularity of LLMs, we systematically evaluate a wide array of LLMs, including GPT family, Claude-3.5, Qwen-2.5 family, ERNIE-4, Moonshot, Yi, Gemma-2, Mistral, InternLM-2.5, DeepSeek, GLM-4, Phi-3 and LlaMa-3.1 family [, , , , , , , , , ].

Response generation.
We employ the official settings and chat template in HuggingFace model card for open-source LLMs.
Proprietary models are assessed via their official API endpoints, using the latest versions as of September 25, 2024.
Considering the varied requirements for diversity and creativity across tasks, we set different temperatures for different tasks. More details can be found in Appendix A.2.1.

Evaluation. We utilize gpt-4o-2024-08-06 to evaluate each generated follow-up response using the weighted checklist and the corresponding reference follow-up response. The evaluation prompt and cases can be found in Appendix A.2.2. To enhance the determinism of the judgment, we set the temperature to 0 and the output length to 4096.

### 3.2 Main Results

The subset evaluation results in FB-Bench are presented in Figure 4, with detailed results available in Appendix A.2.3. The main findings are as follows:

- •
The ranking of LLMs exhibits significant variation between error correction and response maintenance, indicating an unrelated relationship between these two capabilities. For error correction, the ranking of LLMs aligns with people’s perceptions, where closed-source LLMs significantly outperform open-source LLMs. The top three LLMs are closed-source, and Qwen2.5-72B-Instruct achieves the highest score among open-source LLMs but still lags significantly behind the leading closed-source LLM.
Conversely, in response maintenance scenarios, the top four LLMs comprise two open-source LLMs: Qwen2.5-72B-Instruct and internlm2_5-20b-chat, with the former achieving the highest performance among all LLMs.
- •
Some LLMs perform well in error correction yet struggle in response maintenance, indicating they are more susceptible to unreasonable user feedback. LLMs such as Deepseek-V2.5, claude-3-5-sonnet-20240620 and gpt-4o-mini-2024-07-18 exemplify this trend. Specifically, claude-3-5-sonnet-20240620 attains the highest scores in error correction but demonstrates relatively weak response maintenance capability, resulting in its lower ranking.

Figure: Figure 4: The subset evaluation results in FB-Bench between error correction and response maintenance scenarios. Overall denotes the mean of error correction score and response maintenance score.
Refer to caption: x4.png

### 3.3 Analysis

Thanks to our comprehensive taxonomy, we can delve into several critical factors that significantly influence the performance of LLMs on FB-Bench, including interaction scenario types, task types, feedback types and deficiency types.

##### Most LLMs exhibit superior performance in error correction compared to response maintenance.

The performance discrepancy between these two scenarios is illustrated in Figure 5. Generally, LLMs excel in error correction but struggle to maintain consistency in their responses. This disparity is more pronounced among LLMs developed outside of China, even for leading LLMs such as gpt-4o-2024-05-13 and claude-3.5-sonnet-20240620.
This suggests that the majority of LLMs lack a robust capacity to differentiate between valid and misleading instructions. This deficiency could stem from the fact that optimizing an LLM’s instruction-following ability is relatively straightforward; however, overly adherent instruction-following can cause the LLM to distort reality.

Figure: Figure 5: Performance discrepancy between error correction and response maintenance.
Refer to caption: x5.png

##### Advanced LLMs show similar performance on each task in error correction. In contrast, their performance varies widely in response maintenance.

We present the performance scores of several advanced LLMs across different tasks in Figure 6(a) and Figure 6(b).
In error correction scenarios, the scores of different LLMs on each task are relatively close, and they exhibit notably poorer performance on mathematics and reasoning tasks, with scores hovering around 60. Conversely, in response maintenance scenarios, the score discrepancies among different LLMs on each task are more pronounced, particularly in mathematics and reasoning tasks. Remarkably, claude-3-5-sonnet-20240620’s ability in response maintenance is significantly weaker compared to other LLMs, especially in reasoning tasks, despite achieving the highest score in the error correction dimension.

Figure: (a)
Refer to caption: x6.png

##### Pointing out errors directly significantly helps LLMs enhance the quality of responses, while challenging LLMs with fabricated credentials or expertise often mislead them.

We present the performance of several advanced LLMs under various types of human feedback in Figure 7. In error correction scenarios, all LLMs achieve scores higher than 70 when errors are identified by humans. In response maintenance scenarios, all LLMs exhibit poor performance when challenged by humans with fabricated credentials or expertise. Furthermore, it is observable that claude-3-5-sonnet-20240620 significantly outperforms all other LLMs when receiving unreasonable requests, indicating its superior safety capabilities.

Figure: Figure 7: Impact of different feedback types.
Refer to caption: x9.png

##### Advanced LLMs outperform less capable LLMs in addressing all categories of deficiencies, particularly in logic and following instructions.

To deeply investigate the performance disparities in error correction among models, we selected four models that exhibit significant differences in this aspect. Their performance across various deficiency types is depicted in Figure 6(c).
It shows that more advanced LLMs outperform less capable ones in all categories of deficiency.
The primary challenges identified include correcting logical errors and following user instructions, where smaller models underperform even after receiving human feedback. This underperformance is likely attributable to the limited capabilities of smaller models.

## 4 Related Work

##### Evaluation of LLMs

The evaluation of LLMs is essential for their development. It reveals the strengths and weaknesses of existing models and offers key insights and directions for future research. However, most existing studies [, , , , ] focus solely on evaluating the general or specific capabilities of LLMs in single-turn dialogues. They fail to assess LLM performance under various user feedback, which typically involves multi-turn dialogue scenarios. Although there are some benchmarks for multi-turn LLMs [, , , , ], the user inputs in the multi-turn dialogues are often independent, lacking feedback towards to the previous LLM output. Furthermore, much of the data in these multi-turn dialogue benchmarks is synthesized by LLMs, failing to exhibit the diversity and complexity of real-world scenarios.

##### The Importance of Feedback

Human feedback not only enhances model performance but also serves as a critical mechanism for aligning the model with desired outcomes or goals [].
Training models on feedback data, not only can directly enhance the quality of the generated content [] but also allows models to better align with human preferences in style and tone [].
During the inference stage, users can provide feedback on intermediate responses, enabling the model to refine its output until it achieves the user’s satisfaction [, ].
However, a systematic benchmark for evaluating the impact of human feedback on LLMs during the inference stage is still lacking.

##### Benchmarks with Feedback

Several benchmarks have begun to explore the impact of feedback on LLMs. However, they predominantly focus on specific tasks or domains. MINT [] exclusively assesses the coding and reasoning capabilities of LLMs that utilize tools and receive AI-generated language feedback. Intercode [] evaluates the coding skills of LLMs based on feedback from compilers or interpreters executing the code. AgentBench [] examines the reasoning and decision-making abilities of LLMs-as-Agents in response to environmental feedback. Different from prior works, FB-Bench introduces a novel approach by measuring the responsiveness of LLMs to diverse user feedback across a broad spectrum of real-world usage scenarios.

## 5 Conclusion

We introduce FB-Bench, a fine-grained muti-task benchmark for comprehensively evaluating the responsiveness of LLMs to various human feedback across real-world usage scenarios.
A three-tier hierarchical taxonomy, grounded in two popular human-LLM interaction scenarios, is established to ensure thorough coverage of diverse interaction types and scenarios. To facilitate a fine-grained and accurate evaluation, a LLM-as-a-Judge framework, equipped with a weighted checklist, is employed. Benchmarking results from 31 well-known LLMs demonstrate significant performance variations between error correction and response maintenance. Further analysis explores the principal factors influencing the responsiveness of LLMs and provides valuable insights for subsequent research.

## Appendix A Appendix

### A.1 Detailed description of the dataset

#### A.1.1 Query subtask

We further categorize the eight tasks into twenty-four subtasks. Table 2 presents a brief one-sentence description of each subtask.

**Table 2: The description of twenty-four subtasks across eight tasks in FB-Bench.**
| Task | Subtask | Description |
| --- | --- | --- |
| Mathematics | Algebra | Solving algebraic expressions and equations. |
| Geometry | Understanding and applying geometric principles and theorems. |  |
| Equations and inequalities | Solving for variables within equations and inequalities. |  |
| Combinatorial probability | Calculating the likelihood of various combinations and outcomes. |  |
| Arithmetic | Performing basic mathematical operations and number theory. |  |
| Reasoning | Common sense reasoning | Applying everyday knowledge and logic to solve problems. |
| IQ questions | Solving puzzles and questions designed to measure intelligence. |  |
| Coding | Code generation | Automatically writing code snippets for given tasks. |
| Code debugging | Identifying and fixing errors or bugs in code. |  |
| Code knowledge | Understanding programming concepts, languages, and frameworks. |  |
| Text extraction | Information extraction | Extracting structured information from unstructured text. |
| Summary generation | Creating concise summaries of lengthy texts. |  |
| Title extraction | Identifying and extracting the principal titles or headings from documents |  |
| Text error correction | Typo detection | Identifying and correcting misspelled words in the provided text. |
| Text proofreading | Examining texts for errors in logic, factuality, or coherence |  |
| Grammar checking | Identifying and rectifying grammatical errors |  |
| Text creation | Style-based rewriting | Adapting content to different tones, styles, or formats. |
| Generation | Producing coherent, contextually relevant content from scratch. |  |
| Knowledge Q&A | Objective facts Q&A | Providing answers to questions based on factual information. |
| Conceptual explanation | Explaining theories, concepts, or ideas in a comprehensible manner. |  |
| Experiential advice | Offering advice based on personal or shared experiences. |  |
| Logical reasoning | Applying logic to solve problems or answer questions. |  |
| Text translation | Chinese to English | Translating text from Chinese to English accurately. |
| English to Chinese | Translating text from English to Chinese accurately. |  |

#### A.1.2 Detailed data statistics

The distribution of task and subtask categories is shown in Figure 8.

Figure: Figure 8: The distribution of task and subtask categories in FB-Bench.
Refer to caption: x10.png

The length distribution of the four components of conversation in FB-Bench, namely, the user query, the preset model response, user feedback, and the reference follow-up response, is depicted in Figure 9.

Figure: Figure 9: The length distribution of four components in conversations within FB-Bench.
Refer to caption: x11.png

#### A.1.3 Example in FB-Bench

We select an example from error correction and response maintenance scenarios, and display them in Figure 10 and Figure 11, respectively.

Figure: Figure 10: The error correction example in FB-Bench.
Refer to caption: x12.png

Figure: Figure 11: The response maintenance example in FB-Bench.
Refer to caption: x13.png

### A.2 Detailed description of experiments

#### A.2.1 Response generation

We utilize the vllm library [] to deploy open-source LLMs for generating follow-up responses based on a user query, a predetermined model response, and human feedback. In terms of temperature settings, we assign distinct values for different tasks: 0.7 for text creation and text translation, 0.1 for knowledge-based Q&A, and 0 for all other tasks. For the maximum output length, we set it to the minimum value between 4096 and the difference between the LLM context length and the context tokens length.

#### A.2.2 Evaluation

Since FB-Bench focuses on Chinese, we employ a Chinese prompt for evaluation, which is present in Figure 12.

Figure: Figure 12: The evaluation prompt template of FB-Bench.
Refer to caption: x14.png

Figure 13 and Figure 14 show the corresponding evaluation cases within error correction and response maintenance scenarios, respectively.

Figure: Figure 13: The evaluation case in error correction scenario.
Refer to caption: x15.png

Figure: Figure 14: The evaluation case in response maintenance scenario.
Refer to caption: x16.png

#### A.2.3 The full results in FB-Bench

We evaluate 31 popular LLMs using FB-Bench, with the results presented in Table 3.

**Table 3: The full evaluation results in FB-Bench between error correction and response maintenance scenarios. The bold, underlined, and tilde denote the first, second, and third rankings, respectively.**
| Model | Error Correction | Response Maintenance | Overall |
| --- | --- | --- | --- |
| qwen-max-0919 | 71.52 | 62.57 | 67.04 |
| Qwen2.5-72B-Instruct | 66.51 | 63.22 | 64.87 |
| ERNIE-4.0-8K-0329 | 66.30 | 62.59 | 64.44 |
| gpt-4o-2024-05-13 | 69.90 | 55.01 | 62.46 |
| gpt-4-turbo-2024-04-09 | 67.24 | 56.08 | 61.66 |
| Baichuan-Instruct | 65.65 | 57.30 | 61.48 |
| glm-4-0520 | 66.40 | 55.30 | 60.85 |
| Qwen2-72B-Instruct | 63.46 | 57.81 | 60.63 |
| claude-3-5-sonnet-20240620 | 73.87 | 46.34 | 60.11 |
| gpt-4o-mini-2024-07-18 | 66.74 | 50.55 | 58.65 |
| internlm2_5-20b-chat | 55.47 | 60.30 | 57.89 |
| yi-large | 63.28 | 50.91 | 57.10 |
| Mistral-Large-Instruct-2407 | 64.69 | 46.30 | 55.50 |
| moonshot-v1-32k | 59.57 | 51.41 | 55.49 |
| DeepSeek-V2.5 | 64.47 | 46.35 | 55.41 |
| Qwen2.5-7B-Instruct | 55.75 | 49.00 | 52.37 |
| internlm2_5-7b-chat | 49.66 | 54.37 | 52.01 |
| Qwen2-7B-Instruct | 48.05 | 50.71 | 49.38 |
| Yi-1.5-34B-Chat-16K | 48.17 | 46.21 | 47.19 |
| Yi-1.5-9B-Chat-16K | 45.65 | 47.95 | 46.80 |
| claude-3-sonnet-20240229 | 60.48 | 31.98 | 46.23 |
| glm-4-9b-chat | 58.60 | 33.49 | 46.04 |
| Yi-1.5-34B-Chat | 50.55 | 40.69 | 45.62 |
| DeepSeek-Coder-V2-Lite-Instruct | 53.40 | 37.10 | 45.25 |
| Phi-3-medium-4k-instruct | 36.17 | 54.29 | 45.23 |
| Meta-Llama-3.1-70B-Instruct | 56.05 | 32.26 | 44.16 |
| gemma-2-27b-it | 54.31 | 31.69 | 43.00 |
| Yi-1.5-9B-Chat | 47.91 | 34.42 | 41.16 |
| Phi-3-small-8k-instruct | 30.98 | 47.87 | 39.43 |
| gemma-2-9b-it | 51.58 | 24.95 | 38.27 |
| Meta-Llama-3.1-8B-Instruct | 47.31 | 26.26 | 36.78 |

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 5
presentation: 4
questions: Most big questions ended up in the weakness section, however some desired clarifications are shown below:

On lines 270-275, the authors state that GPT-4 was used for annotation. Which GPT-4 is this (0314, 0613, or some turbo version)? Why was GPT-4 used instead of new models which seem to perform better?

On line 473, the authors criticize that data in previous multi-turn benchmarks are synthesized by LLMs. However, it appears that LLMs are used to synthesize a significant portion of FB-Bench’s data (lines 272-275). Why does this same criticism not apply to FB-Bench?

Figure 6 is interesting, but the very light colors on the white background are a bit hard to see. Perhaps slightly darker colors, less line opacity, and points on the axis would make it slightly more readable.

Multiple times in the paper the authors reference “advanced” models. It is a bit ambiguous what “advanced” encompasses. A more explicit definition would be helpful.

In section 2.4, the authors explain their data is from “an online chat service and human preference data”. Could the author please give more detailed information on what these exactly are, why they are reputable, and give examples from the data sources?

In line 261, the authors say they use a heuristic to identify target data, but never specify in any detail what this heuristic is, or why it works.

In line 273, the authors say GPT-4 is used to generate the judgment checklist. How are the weights for the checklist decided? How is the checklist verified? Does removing the checklist significantly change final ranking scores?
soundness: 2
strengths: Originality:
While multi-turn evaluation has been attempted before, the authors present a new perspective focusing on error correction and responses maintenance, which seem less explored.
The authors use a checklist when doing LLM judge evaluations for more finegrain judgments.
Clarity:
The paper is well organized and easy to read, with nice structure and clean figures.
Significance:
The authors find an interesting comparison between error correction and responses maintenance (which inherently seem a bit in tension). The authors find most models have superior error correction performance, suggesting a current weakness in LLMs.
The benchmark itself fills an existing hole for evaluating models on multi-turn conversations.
summary: This paper introduces a new benchmark, FB-Bench, designed to evaluate the responsiveness of LLMs to human feedback in real-world scenarios. The authors argue that existing benchmarks primarily focus on single-turn dialogues or multi-turn dialogues with independent user inputs, which fail to capture the complexity of human feedback in iterative interactions. FB-Bench addresses this gap by offering a more nuanced evaluation framework. FB-Bench is the first systematic benchmark focused on evaluating LLMs' responsiveness to human feedback across diverse real-world tasks. It includes 734 curated samples covering eight task types, five types of response deficiencies, and nine types of user feedback. The authors develop a hierarchical taxonomy that organizes human-LLM interactions into three components: user queries, model responses, and user feedback. This taxonomy supports two main interaction scenarios: error correction (where the model must improve its response based on user feedback) and response maintenance (where the model must maintain a correct response despite potentially misleading feedback). The evaluation is conducted using a weighted checklist specific to each sample, with GPT-4o (August) acting as a judge to score follow-up responses based on human-curated reference responses. The paper evaluates 31 popular LLMs using FB-Bench and reveals significant performance discrepancies between error correction and response maintenance scenarios. Notably, most LLMs perform better in error correction than in maintaining correct responses when faced with misleading or irrelevant feedback.
weaknesses: Out of distribution first turn:
The first turn of the data is collected from some less human preferred response rather than the model tested itself. Naturally, this means the first turn is already out of distribution for the model. It is unclear if this invalidates the real-world applicability of these results. For example, the paper finds Claude-3.5-Sonnet has very low response maintenance (relatively). Would this still happen if Claude-3.5-Sonnet was asked to maintain its own answer? Take another example, Phi-3-small-8k-instruct has very high response maintenance, but low error correction. This would be desired behavior if the model’s own responses were usually correct. However, if the model's response is not fully correct (which is more likely because the model is very small) this response maintenance is actually a negative when deployed in the real world. 

Moreover, just with respect to the pure out of distribution nature of the first turn (you are basically putting someone else’s words in the LLM’s mouth), it may be helpful to see an experiment that is single turn. In this case, the model being tested is given the original user query, and the first response in which the model is asked to correct/maintain the response. This way, no out of distribution tokens are inserted into the assistant section of the sequence. If results in this experiment are similar, it would be sufficient to show that the out of distribution first turn is, at least mechanically, not an issue.


LLM Use in Data Curation:
In lines 271-272 the authors state that GPT-4 was used to find the cause of dissatisfaction when the user’s expectations were not met. However, this feels in contradiction to the conclusion of the paper that LLMs are weak for response maintenance. That is, the human’s expectations could have been wrong or unreasonable, in which the GPT-4 annotator may hallucinate some incorrect cause of dissatisfaction via the same sycophant behavior this paper finds almost every LLM has. Annotation process is not explained in enough detail to convincingly rule out this possibility.


Analysis:
While the results for models are interesting, the analysis focuses on superfluous information rather than deeper insights. For example, the authors write on line 379 that “This disparity [between error correction and response maintenance] is more pronounced among LLMs developed outside of China”. This claim seems a bit out of place, and is not supported with concrete numbers (especially because the benchmark seems to be in Chinese, which will obviously yield better results on models trained for this language, and is a huge confounding variable). Moreover, analysis like this is not particularly tied to the core exploration of error correction vs response maintenance. As a reader, error correction vs maintenance feels more analogous to precision vs recall. Poor performance overall (like Claude-3.5-Sonnet) seems like a calibration issue, not an inherent flaw of the model. For example, any model could achieve 100% success on response maintenance with a system prompt that tells it NEVER to change its initial response. It seems error correction and response maintenance are something that could be calibrated. Looking at Figure 4, it seems “evenly calibrated” models will lie approximately on y=x. However, this assumes that error correction and response maintenance occur at the same rate in the real world, which may not be true. For example, the “smarter” a model gets, the more it should stick to response maintenance rather than error correction, since it can assume it is more skilled with respect to the human user giving feedback. This point connects back to my original critique with out of distribution first turns covered above— we can never know the real aggregate performance of a model without first understanding how often the model will get the first turn incorrect or unsatisfactory. For real-world applicability, the authors should at least consider the tension between error correction and response maintenance in their core analysis. For example, what happens when we use a system prompt that always forces error correction? What about the opposite? Will these get top accuracy in each category if used in conjunction with a suitably strong model?


LLM Judge:
While the usage of the checklist judge is interesting, the authors do not verify the judge's accuracy. This on top of the fact, the checklist itself is GPT-4 generated and seemingly unverified. For example, does the ranking change if we simply change the judge to a different strong model? Why was GPT-4o-08-06 selected as a judge? The authors should have some level of verification for judge quality and simultaneously use this to justify their judge choice.

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confidence: 5
contribution: 2
initial_rating: 3
presentation: 3
questions: 1- What future research directions do the authors anticipate their work will enable?

2- Mention language of your benchmark in the abstract/introduction

3- Line 52: dialugues -> dialogues

4- Some correspondence between Figure 2 and its caption would be nice. The reader benefits from understanding what 2 deficiency types or 9 feedback types refer to in the figure? Is this 2 deficiency or 5?

5- missing citation. There are some previous works introducing the concept of checklist for LLM-as-judge style evaluation and similar post-filterng strategies. It’s nice to acknowledge those work:  
Lin, Bill Yuchen, et al. "WILDBENCH: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild." arXiv preprint arXiv:2406.04770 (2024).
5- providing examples in Table 1 would help the reader to better understand the  user feedback

6- It is not clear how the checklist weights are determined? Seems a bit arbitrarily? 

7- Are there examples of the checklist? Are they defined at instance-level or task-level?

8- Line 377: similar findings repeated twice!

9- Line 379, caption: I guess what the author is trying to convey is that LLMs that are mostly optimized for non-chinese language, are performing worse on FB-Bench? Also worth mentioning in footnote which models are developed in China
soundness: 3
strengths: - This is a useful resource specially for as non-English benchmark further advancing models development for language other than English
- Extensive evaluation of several models, with some interesting findings
summary: The paper introduced a new Chinese multi-turn benchmark to evaluate LLMs appropriate responsiveness to user’s feedback including the ability to correctly fix errors or maintain its correct response when challenged by the user. 
The FB-Bench is built based on a taxonomy covering 8 different task types, 5 different error types and 9 feedback types. 

The author evaluated several different open and closed-sourced models and presented some insights on how current models perform on this task. They used LLM-as-judge to evaluate the responsiveness of LLMs based on a predefined checklist. 

While I appreciate the author's effort in making this benchmark, I am not super impressed by this work as a publication in a top-tier ML conference.
weaknesses: - One major issue I had while reading the paper was that I only realized this is a Chinese resource more than halfway through the paper by going to Appendix. I strongly suggest the author specify the language of your resource early on.
- Some Phrases could be tone-downed a bit, .e.g, three-tier hierarchical taxonomy. For one, I would call this an evaluation framework and not a taxonomy, and I am not sure which aspect of it makes it hierarchical. 
- It would have been nice to extend this to a multilingual benchmark covering other languages

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confidence: 4
contribution: 3
initial_rating: 3
presentation: 2
questions: - Is the feedback human written?
- 2.2.2 Is that before or after the user’s feedback?
- Line 204: is unprofessional the right name?
- 2.3.2.: The scoring of the model response is done based on a checklist? Which? Would have been good to point to the specific checklist.
- 259: human preference data: where does it come from? No citation…
- 267: Who are the annotators? What is the agreement?
- Figure 3 does not show for me at all.
- It would be great to see how fine-grained the feedback actually is and how diverse it is.
soundness: 2
strengths: The main strengths of this paper lie in the originality of the benchmark: To the best of my knowledge, no similar benchmark exists. Additionally, it covers a gap in current LLM research, which is the analysis of LLMs’ ability to engage in meaningful multi-turn conversations, including responding to feedback. I also believe that it is interesting that the paper analyzes how some LLMs are overly responsive to feedback, and do not stay consistent with their answer, even when the feedback is wrong. I also commend the use of human annotators in constructing the benchmark (although I have some comments about that in the weaknesses section).
summary: This paper introduces a benchmark on evaluating LLMs’ responses to human feedback. They collect data over multiple task domains and introduce feedback that either critiques an LLM’s response. The LLM should then respond with either a correction or maintain its response, in cases when the feedback is not justified.
Using their 734 benchmark samples, they evaluate a broad spectrum of current LLMs, using GPT-as-a-judge with a checklist. The paper provides an analysis of the evaluated LLMs’ strengths and weaknesses with respect to feedback.
weaknesses: There are two main weaknesses (besides my smaller questions in the field below) that I would like to highlight.

1. The annotation setup does not seem very rigorous: The paper fails to mention who the human annotators are, how many there are, how much they were paid etc. Additionally, it does not report any IAA or any quality assessment of the benchmark itself (i.e. how can we be sure to be able to trust the quality of the benchmark?) I would have also liked to see a discussion on why a human-LLM set-up was needed and why they have decided not to use a completely human written benchmark? (especially considering the small size of the benchmark - 734 samples)

2. The writing lacks clarity: It is not clear to me what the evaluation setup exactly looks like. Is the input a prompt - response - critique and the output a response to the critique? If yes, does it matter that the first response has potentially not been generated by the same model that will generate the second response? I further would have liked to see an analysis of the “fine-grained” feedback: what makes it fine-grained? (more questions regarding clarity of writing below). The paper also does not mention the specific data sources (i.e. citing the specific datasets).

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confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: NA
soundness: 2
strengths: 1.	This topic is important for better Human-AI collaboration and has not been well-studied previously. 
2.	The paper is well-written and supported by clear illustrations that make it easy to follow.
3.	The observation that models’ capability to do error correction is distinct from their ability to do response maintenance (in Figure 5) is interesting and insightful. I’m surprised by the observation that there is a large gap between models doing error correction (generally good) and their ability to do response maintenance (generally less good), especially “pronounced among LLMs developed outside of China” (line 379).
summary: Human interactions with LLMs are often collaborative and multi-turn in nature. However, existing benchmarks cannot effectively measure the responsiveness of LLMs to human feedback. To solve this research gap, they introduce a new benchmark, aiming to measure LLMs’ responsiveness to various types of fine-grained human feedback, such as error correction.

Authors first collect a set of tasks from various domains (code / math / reasoning) and tasks tasks (extraction, translation, correction, generation, question answering). Then they check model responses how they respond to issues such as poor instruction following, logical errors, factual errors, incomplete answer and lacking in response cohesiveness. Authors also introduce an adversarial case where response are correct to see if models will maintain answers when challenged by users.
weaknesses: 1.	I might be missing something but currently, it’s not stated whether the data contains only the prompts or also the first turn assistant responses (which should ground the feedback). The supplementary material suggests that the first turn assistant responses are fixed but I’m not sure if it’s just for that sample or more generally the case. If it is, which model generated that first turn assistant responses? Furthermore, the data in the supplementary materials suggests that many of the data samples are in Chinese but this was not mentioned in the paper – if there are different languages, please clearly state it in the paper and report the proportion of data in each language. It’s extremely relevant in grounding the understanding of model performance (e.g. models trained by providers based in China might be better in tasks if they contain Chinese). Figure 10 and 11 show identical examples in both Chinese and English – are models tested on both languages?
2.	In line 272, it’s stated that GPT-4 is used to identify issues with responses and provide with annotators refining the pre-annotated elements. The paper fails to state the proportion of cases that the human annotators agree with GPT-4. Ideally, there will be some comparison between GPT4-human agreement and human-human agreement to better understand the reliability of this data curation pipeline. Can the authors also clarify if each sample is reviewed by more than one reviewer? What are the background of these reviewers (e.g. Amazon MTurks?) and what are the guidance given to them to do this task? Currently, it’s missing a lot of critical information as an evaluation dataset contribution.
3.	In line 277, it’s not clear why the authors chose those three models as difficulty filters as they all seem of similar sizes. Also, it will be better to give a sense of the proportion that scores 1 by each model, to get a sense of the filtering rate.
4.	In both the results and analysis sections (3.2 and 3.3), there are no attempt to relate the empirical results to possible explanation identified by other studies, which makes reading the results feel very non-grounded and mostly based on the authors’ guesses/hypotheses. One possible way to relate this to literature is by connecting the low response maintenance rates to the concept of model sycophancy proposed by [1] Another way to relate this is possibly from the cultural lens of what types of behavior are more encouraged in models trained within and outside of China [2] 

[1] https://arxiv.org/abs/2310.13548
[2] https://arxiv.org/abs/2410.02677