# Retrieval Verification: PaperQA_Retrieval_Augmented_Generative_Agent_for_Scientific_Research_parsed

## Pilar: Background
**Query:** _latar belakang masalah, urgensi penelitian, motivasi mendasar, dilatar belakangi oleh, fenomena krusial, konteks situasi saat ini, dan alasan utama studi ini dilakukan._

### Rank 1 (Score: 0.8438)
**Header:** 3.2 TOOLS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Write an answer (answer_length) for the question below based on the provided context. If the context provides insufficient information, reply ‘‘I cannot answer’’. For each part of your answer, indicate which sources most support it via valid citation markers at the end of sentences, like (Example2012). Answer in an unbiased, comprehensive, and scholarly tone. If the question is subjective, provide an opinionated answer in the concluding 1-2 sentences.  
context Extra background information: ask LLM Question: question Answer:  
The question in this prompt is the original question fed to the higher-level PaperQA agent, and the context comes from the collection of relevant chunks within the context library. The response from the the tool is returned to the agent, which may reject or accept the answer based as given in the agent’s initialisation prompt given above.
```

### Rank 2 (Score: 0.8429)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
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.
```

### Rank 3 (Score: 0.8280)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Table 3: **Ablation of LLM Types on LitQA (left).** We compare using different LLMs for the answer LLM and summary LLM, averaged over 2 runs. **Ablation of PaperQA Components on LitQA (right).** _No summary LLM_ setting only implements vector retrieval without any summarizaton and relevance scoring. _Single citation_ only uses the best scoring chunk from the context library. _No ask LLM_ ignores including the LLM’s latent knowledge within the context of the answer LLM. _No search_ setting starts with all papers from LitQA already collected and runs gather ~~e~~ vidence once. _Vanilla RAG_ setting runs the search tool thrice, followed by a single call to gather ~~e~~ vidence and answer ~~q~~ uestion. _Semantic Scholar_ uses Semantic Scholar instead of Google Scholar. _No MC options_ provides no multiple-choice answers as options within the question prompt. All ablations were done with a single run.
```

## Pilar: Methodology
**Query:** _rincian teknis metodologi, arsitektur sistem, desain eksperimen, teknik pengumpulan dataset, algoritma yang diusulkan, sampel, dan prosedur pengujian._

### Rank 1 (Score: 0.8233)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
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.
```

### Rank 2 (Score: 0.8202)
**Header:** C.2 FULL-TEXT RETRIEVAL METRIC / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
As the abstract retrieval from Appendix C.1 does not sufficiently cover all the use cases of PaperQA, we also use an earlier version of LitQA to evaluate our search pipeline, but now considering questions that are synthesized from articles’ bodies and not abstracts. Moreover, we examine the ability of our pipeline to find, access and parse the original papers of these questions.
```

### Rank 3 (Score: 0.8182)
**Header:** C.1 ABSTRACT RETRIEVAL METRIC / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
A concatenated example of such abstract-question pair (red being the LLM completion) follows:  
Title: DeepDTA: deep drug-target binding affinity prediction Abstract: Motivation: The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. [...] One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). Results: The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. [...]  
Question: Are there any models that use 1D representations of targets and drugs for drug target binding affinity prediction?  
This dataset is then used to search for the top-10 papers using 20 keywords that were generated using the following prompt:  
We want to answer the following question: question Provide num_keywords unique keyword searches (one search per line) and year ranges that will find papers to help answer the question. Do not use boolean operators. Make sure not to repeat searches without changing the keywords or year ranges. Make some searches broad and some narrow. The current year is get_year(). Use this format: ‘X. [keywords], [start year]-[end year]‘ For example, a list of 3 keywords would be formatted as: 1. ‘keyword1, keyword2, 2020-2021 2. ‘keyword3, keyword4, keyword5, 2020-2021 3. ‘keyword1, keyword2, 2020-2021‘
```

## Pilar: Gap
**Query:** _keterbatasan penelitian terdahulu, kelemahan metode sebelumnya, kekosongan literatur, masalah yang belum terselesaikan, namun demikian, akan tetapi, dan hasil yang belum jelas._

### Rank 1 (Score: 0.8429)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
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.
```

### Rank 2 (Score: 0.8250)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
Table 4: **Comparison of Hallucination in Citations.** We compare PaperQA’s hallucination rates of citations with three pre-trained LLMs. _Hallucinated_ refers to _references_ that are either nonexistent, partially inaccurate, or have content that does not match the claim. Scores are calculated for each LLM based on N number of citations provided by the model. _[∗]_ For context irrelevance only, all 237 citations were evaluated via GPT-4, with 25 additionally checked by experts and 0 were found to be irrelevant.  



Lastly, due to the retrieval-first nature of LitQA, where only a single chunk from the original paper includes the answer, both _single citation_ and _no summary LLM_ settings perform comparably to standard PaperQA.
```

### Rank 3 (Score: 0.8247)
**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.
```

## Pilar: Objective
**Query:** _tujuan utama penelitian, sasaran spesifik, kontribusi inti makalah, hasil yang ingin dicapai, mengeksplorasi, dan solusi yang diusulkan untuk menyelesaikan masalah._

### Rank 1 (Score: 0.8326)
**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.
```

### Rank 2 (Score: 0.8295)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
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.
```

### Rank 3 (Score: 0.8283)
**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: Futurework
**Query:** _saran untuk penelitian selanjutnya, rekomendasi pekerjaan di masa depan, arah pengembangan masa depan, dan peluang studi berikutnya untuk mengatasi batasan saat ini._

### Rank 1 (Score: 0.8302)
**Header:** C.1 ABSTRACT RETRIEVAL METRIC / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
A concatenated example of such abstract-question pair (red being the LLM completion) follows:  
Title: DeepDTA: deep drug-target binding affinity prediction Abstract: Motivation: The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. [...] One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). Results: The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. [...]  
Question: Are there any models that use 1D representations of targets and drugs for drug target binding affinity prediction?  
This dataset is then used to search for the top-10 papers using 20 keywords that were generated using the following prompt:  
We want to answer the following question: question Provide num_keywords unique keyword searches (one search per line) and year ranges that will find papers to help answer the question. Do not use boolean operators. Make sure not to repeat searches without changing the keywords or year ranges. Make some searches broad and some narrow. The current year is get_year(). Use this format: ‘X. [keywords], [start year]-[end year]‘ For example, a list of 3 keywords would be formatted as: 1. ‘keyword1, keyword2, 2020-2021 2. ‘keyword3, keyword4, keyword5, 2020-2021 3. ‘keyword1, keyword2, 2020-2021‘
```

### Rank 2 (Score: 0.8291)
**Header:** C.1 ABSTRACT RETRIEVAL METRIC / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
We thus evaluate recall, i.e. finding the original paper. Figure 2 shows the cumulative recall curve, where Google Scholar and Semantic Scholar show outstanding ability to retrieve the original paper. Although CORE API [78] does not perform on par with the other two, we include it here as their main contribution to the field of scientific literature is their standardisation of open-access article repositories.
```

### Rank 3 (Score: 0.8272)
**Header:** 5.2 RESULTS / PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
**Content:**
```text
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.
```
