# Retrieval Verification: PaperQA_Retrieval_Augmented_Generative_Agent_for_Scientific_Research_parsed

## Pilar: Background
**Query:** _Fenomena ini merupakan isu krusial. Oleh karena itu, studi ini dilakukan untuk merespons urgensi permasalahan yang melatarbelakangi penelitian._

### Rank 1 (Score: 0.8098)
**Header:** 4 THE LITQA DATASET / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Existing benchmarks for scientific question-answering evaluate the ability of AI systems to present widely-known or widely-available information, or to answer questions given a specific context. These benchmarks are thus insufficient for evaluating an agent’s ability to answer questions based on retrieved information. We thus introduce the LitQA dataset for PaperQA evaluation. The questions in LitQA are sourced from recent literature (after September 2021), so that we expect that they will not have been including in training for most language models, and are designed to be difficult or impossible to answer without retrieving the relevant paper. LitQA thus evaluates the ability both to retrieve necessary information and to present an accurate answer based on that information.  
**Dataset description** The LitQA dataset consists of 50 multiple-choice questions, assembled by experts. All questions come from the biomedical domain. It has 5 Yes/No questions, 6 questions with 3 possible answers, 23 questions with 4 possible answers, 10 questions with 5 possible answers, 4 questions with 6 possible answers and finally 2 questions with 7 possible answers. We show examples of the questions in Table 1.  
**Data collection** All questions were written and reviewed by researchers in natural and biomedical sciences. Questions were assembled by first selecting a paper published after the September 2021
```

### Rank 2 (Score: 0.8076)
**Header:** gather ~~e~~ vidence description: / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Give a specific question to get evidence for it. This will increase evidence and relevant paper counts.
```

### Rank 3 (Score: 0.8064)
**Header:** 5 EXPERIMENTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
In this section we measure the performance of PaperQA on LitQA and other datasets, and compare to existing commercial products. We ablate all components of PaperQA on the LitQA dataset, showing their importance. Finally, we investigate the ability to retrieve relevant papers and the rate of hallucinations.
```

## Pilar: Methodology
**Query:** _Penelitian ini menggunakan pendekatan terstruktur di mana data dikumpulkan dan analisis dilakukan menggunakan teknik serta metode yang dijelaskan pada bagian ini._

### Rank 1 (Score: 0.8529)
**Header:** ACKNOWLEDGMENTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
The authors thank Mitchell Zheng at the University of Melbourne for contributing questions to the LitQA dataset. We also thank Matthew Rubashkin for significant contribution to the results and engineering of this paper.
```

### Rank 2 (Score: 0.8365)
**Header:** 4 THE LITQA DATASET / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Existing benchmarks for scientific question-answering evaluate the ability of AI systems to present widely-known or widely-available information, or to answer questions given a specific context. These benchmarks are thus insufficient for evaluating an agent’s ability to answer questions based on retrieved information. We thus introduce the LitQA dataset for PaperQA evaluation. The questions in LitQA are sourced from recent literature (after September 2021), so that we expect that they will not have been including in training for most language models, and are designed to be difficult or impossible to answer without retrieving the relevant paper. LitQA thus evaluates the ability both to retrieve necessary information and to present an accurate answer based on that information.  
**Dataset description** The LitQA dataset consists of 50 multiple-choice questions, assembled by experts. All questions come from the biomedical domain. It has 5 Yes/No questions, 6 questions with 3 possible answers, 23 questions with 4 possible answers, 10 questions with 5 possible answers, 4 questions with 6 possible answers and finally 2 questions with 7 possible answers. We show examples of the questions in Table 1.  
**Data collection** All questions were written and reviewed by researchers in natural and biomedical sciences. Questions were assembled by first selecting a paper published after the September 2021
```

### Rank 3 (Score: 0.8286)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
|AutoGPT<br>3|20.7<br>7.3<br>22.0|41.4%<br>73.9%|||
|Elicit<br>1<br>Scite<br>1<br>Perplexity<br>1<br>Perplexity (Co-pilot)<br>1|12.0<br>16.0<br>22.0<br>12.0<br>21.0<br>17.0<br>9.0<br>10.0<br>31.0<br>29.0<br>10.0<br>12.0|24.0%<br>42.9%<br>24.0%<br>36.4%<br>18.0%<br>47.4%<br>58.0%<br>74.4%|||
|PaperQA<br>4|34.8<br>4.8<br>10.5|**69.5%**<br>**87.9%**|||  
and Perplexity – in Table 2. All commercial tools are specifically tailored to answering questions by retrieving scientific literature. We give them the same prompt as to PaperQA. From Table 2 we see that PaperQA outperforms all competing models and products, and is on par with that of human experts with access to the internet. Furthermore, we see the lowest rate of incorrectly answered questions out of all tools, which rivals that of humans. This emphasizes that PaperQA is better calibrated to express uncertainty when it actually is uncertain. Surprisingly, GPT-4 and Claude-2 perform better than random although the questions are from papers after their training cut-off date, suggesting they have latent knowledge, leading to useful bias towards answers that are more plausible.
```

## Pilar: Gap
**Query:** _Meskipun banyak studi telah dilakukan, masih terdapat kekosongan literatur (gap), kelemahan, dan batasan pada penelitian sebelumnya yang belum dieksplorasi._

### Rank 1 (Score: 0.8182)
**Header:** 6 LIMITATIONS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
PaperQA leverages fact, processes and concepts from underlying research papers; transforming that information into human and machine interpretable context. We have an underlying assumption from this that the information in the underlying papers is correct, which may not hold. Although we give some “signals” to the model like the journal name and citation count, these are not faithful indicators of quality.  
Our models and benchmarks are affected by the changing nature of science and the availability of scientific literature: some of the questions in LitQA may have new correct answers or become invalid over time. Users of this benchmark should therefore limit papers used to answer the questions to be cutoff at September 15, 2023.  
While recent work has been conducted on prompt optimization [75, 76], the complex setting of multiple agents with individual prompts is unsolved. Specifically, the task becomes a non-trivial, bi-level optimization problem. Consequently, discerning the impact of manual prompt adjustments becomes difficult. Thus, it is unlikely our prompts are optimal and it is difficult to assess which pieces of the prompts are necessary.
```

### Rank 2 (Score: 0.8107)
**Header:** 1 INTRODUCTION / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
The rate of papers published yearly grows at an exponential rate, with over 5 million academic articles published in 2022 [1], and over 200 million articles in total [2]. The difficulty of navigating this extensive literature means significant scientific findings have gone unnoticed for extended periods [3, 4, 5]. Work in the last 10 years has sought to make the space of literature more manageable for scientists, with the introduction of keyword search systems [6, 7], vector similarity embeddings [8, 9, 10, 11] and recommender systems [12, 13]. The process of scientific discovery from literature is still, however, highly manual.  
The use of Large Language Models (LLMs) to answer scientific questions is increasingly seen in academia and research-heavy professions such as medicine [14, 15]. While LLMs can produce answers faster and encompass a broader, deeper scope than manual searching, there is a high risk of hallucination in responses, which can lead to potentially dangerous outcomes [16, 17]. Incorrect information can be more damaging than no information at all, as the time to verify veracity can take just as long as retrieving it from research papers in the first place [18]. Reliance on pre-trained LLMs also prevents the discovery of new information published after a training cutoff date. Given the rapidly moving pace of science, this can lead to misconceptions persisting.
```

### Rank 3 (Score: 0.7997)
**Header:** 7 CONCLUSION / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
We introduced PaperQA, a Retrieval-Augmented Generative (RAG) agent that can answer scientific questions better than other LLMs and commercial products. We found PaperQA to be more costefficient than humans, while still retaining its accuracy on par with human researchers. We measured the hallucination rate of citations for recent LLMs to be between 40-60%, whereas we were not able to find a single hallucinated citation in PaperQA’s responses. We also introduced LitQA – a benchmark of 50 questions that require retrieving information from full-text scientific papers. The PaperQA system works independently of the underlying model, with various combinations of Claude-2, GPT-3.5, and GPT-4 providing strong results on LitQA. The most important attributes of PaperQA are its ability to dynamically use RAG tools, retrieve full-text papers, and iterate on the answer through the agent’s decision-making. We hope this open-source implementation of a scientific question-answering system illuminates the design of future RAG agents and tools that reduce hallucinations in LLMs. With such advancements, we believe scientific research will be carried at a fraction of the cost and a multiple of the speed, spurring faster innovation within the natural sciences. By augmenting researchers with a literature review tool, we aim to minimise the time spend searching literature, and rather maximize the amount of productive hours spend carrying out deep thinking for scientific research.
```

## Pilar: Objective
**Query:** _Tujuan utama dan sasaran spesifik dari artikel ini adalah untuk memberikan kontribusi baru, mengeksplorasi, dan menyelesaikan permasalahan tersebut._

### Rank 1 (Score: 0.8372)
**Header:** ACKNOWLEDGMENTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
The authors thank Mitchell Zheng at the University of Melbourne for contributing questions to the LitQA dataset. We also thank Matthew Rubashkin for significant contribution to the results and engineering of this paper.
```

### Rank 2 (Score: 0.8203)
**Header:** 5 EXPERIMENTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
In this section we measure the performance of PaperQA on LitQA and other datasets, and compare to existing commercial products. We ablate all components of PaperQA on the LitQA dataset, showing their importance. Finally, we investigate the ability to retrieve relevant papers and the rate of hallucinations.
```

### Rank 3 (Score: 0.8191)
**Header:** 4 THE LITQA DATASET / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Existing benchmarks for scientific question-answering evaluate the ability of AI systems to present widely-known or widely-available information, or to answer questions given a specific context. These benchmarks are thus insufficient for evaluating an agent’s ability to answer questions based on retrieved information. We thus introduce the LitQA dataset for PaperQA evaluation. The questions in LitQA are sourced from recent literature (after September 2021), so that we expect that they will not have been including in training for most language models, and are designed to be difficult or impossible to answer without retrieving the relevant paper. LitQA thus evaluates the ability both to retrieve necessary information and to present an accurate answer based on that information.  
**Dataset description** The LitQA dataset consists of 50 multiple-choice questions, assembled by experts. All questions come from the biomedical domain. It has 5 Yes/No questions, 6 questions with 3 possible answers, 23 questions with 4 possible answers, 10 questions with 5 possible answers, 4 questions with 6 possible answers and finally 2 questions with 7 possible answers. We show examples of the questions in Table 1.  
**Data collection** All questions were written and reviewed by researchers in natural and biomedical sciences. Questions were assembled by first selecting a paper published after the September 2021
```

## Pilar: Futurework
**Query:** _Keterbatasan dalam penelitian ini membuka peluang bagi studi selanjutnya. Penelitian di masa depan disarankan untuk memperluas cakupan dan menyelesaikan isu terkait._

### Rank 1 (Score: 0.7986)
**Header:** 5 EXPERIMENTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
In this section we measure the performance of PaperQA on LitQA and other datasets, and compare to existing commercial products. We ablate all components of PaperQA on the LitQA dataset, showing their importance. Finally, we investigate the ability to retrieve relevant papers and the rate of hallucinations.
```

### Rank 2 (Score: 0.7986)
**Header:** REPRODUCIBILITY STATEMENT / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Discussions on the inherent limitations and challenges faced, along with the mitigations applied to maintain the robustness and reliability of the models, are available in section 6. For comprehensive understanding and replicability of our approach, we recommend a thorough review of the mentioned section, coupled with the supplemental materials provided.
```

### Rank 3 (Score: 0.7960)
**Header:** 1 INTRODUCTION / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
The rate of papers published yearly grows at an exponential rate, with over 5 million academic articles published in 2022 [1], and over 200 million articles in total [2]. The difficulty of navigating this extensive literature means significant scientific findings have gone unnoticed for extended periods [3, 4, 5]. Work in the last 10 years has sought to make the space of literature more manageable for scientists, with the introduction of keyword search systems [6, 7], vector similarity embeddings [8, 9, 10, 11] and recommender systems [12, 13]. The process of scientific discovery from literature is still, however, highly manual.  
The use of Large Language Models (LLMs) to answer scientific questions is increasingly seen in academia and research-heavy professions such as medicine [14, 15]. While LLMs can produce answers faster and encompass a broader, deeper scope than manual searching, there is a high risk of hallucination in responses, which can lead to potentially dangerous outcomes [16, 17]. Incorrect information can be more damaging than no information at all, as the time to verify veracity can take just as long as retrieving it from research papers in the first place [18]. Reliance on pre-trained LLMs also prevents the discovery of new information published after a training cutoff date. Given the rapidly moving pace of science, this can lead to misconceptions persisting.
```
